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This is my report on Effect of stepwise adjustment of Damping factor upon PageRank (v1). While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli. Abstract — The effect of adjusting damping factor α, from a small initial value α0 to the final desired αf value, upon then iterations needed for PageRank computation is observed. Adjustment of the damping factor is done in one or more steps. Results show no improvement in performance over a fixed damping factor based PageRank. Index terms — PageRank algorithm, Step-wise adjustment, Damping factor.

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Adjusting PageRank parameters and comparing results : REPORT

This is my report on Adjusting PageRank parameters and comparing results (version 1).
While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli.
Web graphs unaltered are reducible, and thus the rate of convergence of the power-iteration method is the rate at which αk → 0, where α is the damping factor, and k is the iteration count. An estimate of the number of iterations needed to converge to a tolerance τ is logα τ. For τ = 10-6 and α = 0.85, it can take roughly 85 iterations to converge. For α = 0.95, and α = 0.75, with the same tolerance τ = 10-6, it takes roughly 269 and 48 iterations respectively. For τ = 10-9, and τ = 10-3, with the same damping factor α = 0.85, it takes roughly 128 and 43 iterations respectively. Thus, adjusting the damping factor or the tolerance parameters of the PageRank algorithm can have a significant effect on the convergence rate, both in terms of time and iterations. However, especially with the damping factor α, adjustment of the parameter value is a delicate balancing act. For smaller values of α, the convergence is fast, but the link structure of the graph used to determine ranks is less true. Slightly different values for α can produce very different rank vectors. Moreover, as α → 1, convergence slows down drastically, and sensitivity issues begin to surface [langville04].

Sensor Fusion Study - Ch7. Kalman Filter Generalizations [김영범]

This document discusses several generalizations and modifications that can be made to the standard Kalman filter. Section 7.3 describes how a steady-state Kalman filter can be used instead of a time-varying filter when system dynamics are time-invariant. Section 7.4 discusses a fading memory filter that discounts older measurements to address cases when system dynamics are imperfectly known. Section 7.5 presents several approaches to incorporate state equality and inequality constraints into the Kalman filter formulation, including model reduction, projection approaches, and probability density function truncation.

Sensor Fusion Study - Ch13. Nonlinear Kalman Filtering [Ahn Min Sung]

1. The document discusses various nonlinear Kalman filtering techniques, including the extended Kalman filter (EKF), iterated EKF, and second-order EKF.
2. The EKF linearizes the system equations around the current state estimate to apply the Kalman filter equations. Higher-order approaches do additional Taylor series expansions.
3. Parameter estimation with nonlinear filters is also covered, where an augmented state vector is used to jointly estimate the system state and unknown parameters.

Adjusting PageRank parameters and comparing results : REPORT

This document discusses experiments adjusting PageRank algorithm parameters and comparing results. It is found that increasing the damping factor α increases iterations exponentially, while decreasing the tolerance τ decreases iterations exponentially. PageRank with L∞ norm converges fastest on average, followed by L2 norm then L1 norm. The most stable method for comparing relative performance is the geometric mean ratio.

Sensor Fusion Study - Ch8. The Continuous-Time Kalman Filter [이해구]

This chapter discusses the continuous-time Kalman filter. It begins by comparing discrete-time and continuous-time systems, then derives the continuous-time Kalman filter equations. It also describes alternative methods for solving the Riccati equation such as transition matrix approach and square root filtering. Finally, it generalizes the continuous-time Kalman filter to cases with correlated process and measurement noise, as well as colored noise.

Sensor Fusion Study - Ch5. The discrete-time Kalman filter [박정은]

The document summarizes key concepts about the discrete-time Kalman filter. It describes how the Kalman filter uses a set of mathematical equations to estimate the state of a dynamic system based on a series of measurements over time that contain noise. These equations estimate the state mean and covariance to minimize the error between the true state and the estimated state. The derivation of the filter equations is shown, including the time update and measurement update equations. Issues like divergence due to modeling errors and numerical problems are discussed, along with remedies like adding fictitious process noise.

Sensor Fusion Study - Ch10. Additional topics in kalman filter [Stella Seoyeo...

This document discusses additional topics related to Kalman filtering, including verifying filter performance, multiple model estimation, reduced-order filtering, robust filtering, and handling delayed measurements. Specific topics covered include using innovations statistics to verify filters, running multiple filters in parallel with different models, reducing filter order to lower computational costs, making filters more robust to model uncertainties, and modifying filters to incorporate out-of-sequence measurements.

Adjusting PageRank parameters and comparing results : REPORT

This is my report on Adjusting PageRank parameters and comparing results (v2).
While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli.
Abstract — The effect of adjusting damping factor α and tolerance τ on iterations needed for PageRank computation is studied here. Relative performance of PageRank computation with L1, L2, and L∞ norms used as convergence check, are also compared with six possible mean ratios. It is observed that increasing the damping factor α linearly increases the iterations needed almost exponentially. On the other hand, decreasing the tolerance τ exponentially decreases the iterations needed almost exponentially. On average, PageRank with L∞ norm as convergence check is the fastest, quickly followed by L2 norm, and then L1 norm. For large graphs, above certain tolerance τ values, convergence can occur in a single iteration. On the contrary, below certain tolerance τ values, sensitivity issues can begin to appear, causing computation to halt at maximum iteration limit without convergence. The six mean ratios for relative performance comparison are based on arithmetic, geometric, and harmonic mean, as well as the order of ratio calculation. Among them GM-RATIO, geometric mean followed by ratio calculation, is found to be most stable, followed by AM-RATIO.
Index terms — PageRank algorithm, Parameter adjustment, Convergence function, Sensitivity issues, Relative performance comparison.

Adjusting PageRank parameters and comparing results : REPORT

This is my report on Adjusting PageRank parameters and comparing results (version 1).
While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli.
Web graphs unaltered are reducible, and thus the rate of convergence of the power-iteration method is the rate at which αk → 0, where α is the damping factor, and k is the iteration count. An estimate of the number of iterations needed to converge to a tolerance τ is logα τ. For τ = 10-6 and α = 0.85, it can take roughly 85 iterations to converge. For α = 0.95, and α = 0.75, with the same tolerance τ = 10-6, it takes roughly 269 and 48 iterations respectively. For τ = 10-9, and τ = 10-3, with the same damping factor α = 0.85, it takes roughly 128 and 43 iterations respectively. Thus, adjusting the damping factor or the tolerance parameters of the PageRank algorithm can have a significant effect on the convergence rate, both in terms of time and iterations. However, especially with the damping factor α, adjustment of the parameter value is a delicate balancing act. For smaller values of α, the convergence is fast, but the link structure of the graph used to determine ranks is less true. Slightly different values for α can produce very different rank vectors. Moreover, as α → 1, convergence slows down drastically, and sensitivity issues begin to surface [langville04].

Sensor Fusion Study - Ch7. Kalman Filter Generalizations [김영범]

This document discusses several generalizations and modifications that can be made to the standard Kalman filter. Section 7.3 describes how a steady-state Kalman filter can be used instead of a time-varying filter when system dynamics are time-invariant. Section 7.4 discusses a fading memory filter that discounts older measurements to address cases when system dynamics are imperfectly known. Section 7.5 presents several approaches to incorporate state equality and inequality constraints into the Kalman filter formulation, including model reduction, projection approaches, and probability density function truncation.

Sensor Fusion Study - Ch13. Nonlinear Kalman Filtering [Ahn Min Sung]

1. The document discusses various nonlinear Kalman filtering techniques, including the extended Kalman filter (EKF), iterated EKF, and second-order EKF.
2. The EKF linearizes the system equations around the current state estimate to apply the Kalman filter equations. Higher-order approaches do additional Taylor series expansions.
3. Parameter estimation with nonlinear filters is also covered, where an augmented state vector is used to jointly estimate the system state and unknown parameters.

Adjusting PageRank parameters and comparing results : REPORT

This document discusses experiments adjusting PageRank algorithm parameters and comparing results. It is found that increasing the damping factor α increases iterations exponentially, while decreasing the tolerance τ decreases iterations exponentially. PageRank with L∞ norm converges fastest on average, followed by L2 norm then L1 norm. The most stable method for comparing relative performance is the geometric mean ratio.

Sensor Fusion Study - Ch8. The Continuous-Time Kalman Filter [이해구]

This chapter discusses the continuous-time Kalman filter. It begins by comparing discrete-time and continuous-time systems, then derives the continuous-time Kalman filter equations. It also describes alternative methods for solving the Riccati equation such as transition matrix approach and square root filtering. Finally, it generalizes the continuous-time Kalman filter to cases with correlated process and measurement noise, as well as colored noise.

Sensor Fusion Study - Ch5. The discrete-time Kalman filter [박정은]

The document summarizes key concepts about the discrete-time Kalman filter. It describes how the Kalman filter uses a set of mathematical equations to estimate the state of a dynamic system based on a series of measurements over time that contain noise. These equations estimate the state mean and covariance to minimize the error between the true state and the estimated state. The derivation of the filter equations is shown, including the time update and measurement update equations. Issues like divergence due to modeling errors and numerical problems are discussed, along with remedies like adding fictitious process noise.

Sensor Fusion Study - Ch10. Additional topics in kalman filter [Stella Seoyeo...

This document discusses additional topics related to Kalman filtering, including verifying filter performance, multiple model estimation, reduced-order filtering, robust filtering, and handling delayed measurements. Specific topics covered include using innovations statistics to verify filters, running multiple filters in parallel with different models, reducing filter order to lower computational costs, making filters more robust to model uncertainties, and modifying filters to incorporate out-of-sequence measurements.

Adjusting PageRank parameters and comparing results : REPORT

This is my report on Adjusting PageRank parameters and comparing results (v2).
While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli.
Abstract — The effect of adjusting damping factor α and tolerance τ on iterations needed for PageRank computation is studied here. Relative performance of PageRank computation with L1, L2, and L∞ norms used as convergence check, are also compared with six possible mean ratios. It is observed that increasing the damping factor α linearly increases the iterations needed almost exponentially. On the other hand, decreasing the tolerance τ exponentially decreases the iterations needed almost exponentially. On average, PageRank with L∞ norm as convergence check is the fastest, quickly followed by L2 norm, and then L1 norm. For large graphs, above certain tolerance τ values, convergence can occur in a single iteration. On the contrary, below certain tolerance τ values, sensitivity issues can begin to appear, causing computation to halt at maximum iteration limit without convergence. The six mean ratios for relative performance comparison are based on arithmetic, geometric, and harmonic mean, as well as the order of ratio calculation. Among them GM-RATIO, geometric mean followed by ratio calculation, is found to be most stable, followed by AM-RATIO.
Index terms — PageRank algorithm, Parameter adjustment, Convergence function, Sensitivity issues, Relative performance comparison.

Meeting w3 chapter 2 part 1

This document provides an overview of analog control systems and Laplace transforms. It introduces key concepts like Laplace transforms, common time domain inputs, transfer functions, and modeling electrical, mechanical and electromechanical systems using block diagrams and mathematical models. Examples are provided to illustrate Laplace transforms, transfer functions, and analyzing system response using poles, zeros and stability analysis.

Sensor Fusion Study - Ch3. Least Square Estimation [강소라, Stella, Hayden]

This document discusses Wiener filtering and recursive least squares estimation. It begins with an introduction to Wiener filtering, providing an overview of its history and development. It then discusses how the power spectrum of a stochastic process changes when passed through a linear time-invariant system. Next, it formulates the problem of using a linear filter to extract a signal from additive noise. It derives expressions for the power spectrum of the error and its variance. Finally, it considers optimizing a parametric filter by assuming the optimal filter is a first-order low-pass filter and that the signal and noise spectra are known forms. It derives an expression for the optimal parameter T based on minimizing the error variance.

AR model

This document discusses methods for selecting the order of an autoregressive (AR) model. It explains that AR models depend only on previous outputs and have poles but no zeros. Several criteria for selecting the optimal AR model order are presented, including the Akaike Information Criterion (AIC) and Finite Prediction Error (FPE) criterion. Higher order models fit the data better but can introduce spurious peaks, so the goal is to minimize criteria like AIC or FPE to find the best balance. The document concludes that while these criteria provide guidance, the optimal order depends on the specific data, and inconsistencies can exist between the different methods.

Sensor Fusion Study - Ch15. The Particle Filter [Seoyeon Stella Yang]

The particle filter is a statistical approach to estimation that works well for problems where the Kalman filter fails due to nonlinearities. It approximates the conditional probability distribution of the state using weighted particles. The weights are updated using Bayes' rule based on new measurements. However, particle filters can suffer from sample impoverishment over time, where most particles have negligible weight. Various techniques like roughening, prior editing, and Markov chain Monte Carlo resampling are used to address this issue.

Webinar on Demystifying Data Acquistion Systems: Access Data through Matlab, ...

Webinar on Demystifying Data Acquistion Systems: Access Data through Matlab, ...Manipal Institute of Technology

This document discusses a webinar on demystifying data acquisition and accessing data through LabVIEW, MATLAB, and Simulink. The webinar covers basics of data acquisition systems including sampling rate, aliasing, resolution, range, and normalization. It also discusses various hardware options for data acquisition and interfacing data acquisition devices with software platforms like MATLAB, Simulink, and LabVIEW.Chapter 7

This document discusses methods for detecting autocorrelation in econometrics models. There are two main approaches: graphical methods which involve plotting residuals over time or against lagged residuals, and numerical methods like the Durbin-Watson test and Breusch-Godfrey test. The Durbin-Watson test checks for first-order autocorrelation while the Breusch-Godfrey test allows testing for autocorrelation at different orders. Both tests analyze the residuals from an ordinary least squares regression to determine if autocorrelation is present based on the test statistic value and p-value.

Bioesta

Reaction time experiments were conducted under different initial vehicle velocities and road surface conditions to measure a driver's delay in braking response after seeing brake lights. Ten trials were run for each combination of initial velocity (60 km/hr and 120 km/hr) and frictional coefficient (0.8 for dry conditions and 0.4 for rainy conditions). The delay time and distance traveled before braking were recorded to analyze a person's reaction time under various scenarios.

observer based state feedback controller design in Matlab (Simulink).

In this lecture observer based feedback controller is designed in matlab Simulink
For complete design procedure Watch Video
https://youtu.be/Lax3etc837U

Data mining model

This document discusses Receiver Operating Characteristic (ROC) curves and how they are used to evaluate machine learning models and predictions. It explains that ROC curves plot the true positive rate against the false positive rate for different classification thresholds. The area under the ROC curve (AUC) indicates the model's ability to correctly classify examples, with higher values indicating better performance. The document then provides a Python example to generate ROC curves for random forest and naive Bayes classifiers trained on a synthetic dataset, finding that the naive Bayes model has the best performance with an AUC of 0.993.

Stats computing project_final

The document summarizes a project analyzing factors that impact the landing distance of commercial flights. It includes:
- Combining and cleaning two datasets of 950 flight landings, removing duplicates and abnormal values
- Descriptive analysis finding linear relationships between distance and ground speed, and less linear relationships with other factors
- Linear regression identifying ground speed, height, and aircraft type as significant factors impacting landing distance
- The final model uses 832 observations and explains landing distance based on ground speed, height, and whether the aircraft is an Airbus or Boeing

recursive problem_solving

This document discusses recursive problem solving, where a problem is broken down into smaller instances of the same problem. For a recursive procedure to work, it must have a base case - an input where the solution is already known. The procedure recursively applies itself to progressively smaller inputs until the base case is reached. This evaluation "spirals" inward until the base case output unwinds back to the original call. The example procedure g recursively counts down from its input n to 0 before returning 1.

Lesson 4 ar-ma

This document discusses autocorrelation models and their applications in Python. It describes the autocorrelation function (ACF) and partial autocorrelation function (PACF), and how they are used to identify autoregressive (AR) and moving average (MA) time series models. AR models regress the current value on prior values, while MA models regress the current value on prior noise terms. The document demonstrates how to interpret ACF and PACF plots to select AR or MA models, and how to fit these models in Python.

Applications of derivatives

Derivatives can be used for several applications:
1) Finding increasing or decreasing intervals by taking the derivative and setting it equal to zero.
2) Locating local minima and maxima by taking the derivative, setting it equal to zero, and testing values on either side.
3) Finding absolute minima and maxima on an interval by checking endpoints and local minima/maxima.
4) Determining concavity by taking the second derivative and whether it is positive or negative.
5) Locating points of inflection where the graph changes concavity by setting the second derivative equal to zero.
6) Relating position, velocity, and acceleration through their derivatives.
7

Rank adjustment strategies for Dynamic PageRank : REPORT

This is my report on:
Rank adjustment strategies for Dynamic PageRank (v1).
While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli.
Abstract — To avoid calculating ranks of vertices in a dynamic graph from scratch for every snapshot, the ones computed in the previous snapshot of the graph can be used, with adjustment. Four different rank adjustment strategies for dynamic PageRank are studied here. These include zero-fill, 1/N-fill, scaled zero-fill, and scaled 1/N-fill. Results indicate that the scaled 1/N-fill strategy requires the least number of iterations, on average. As long as the graph has no affected dead ends (including dead ends in the previous snapshot), unaffected vertices can be skipped with this adjustment strategy.
Index terms — PageRank algorithm, Dynamic graph, Rank adjustment, Initial ranks.

Integration

Integrals are used to find the area or volume bounded by lines and can be definite or indefinite. There are several methods for solving integrals including the reverse power rule, substitution method, and evaluating definite integrals by substituting the bounds. Indefinite integrals are anti-derivatives with an added constant, while definite integrals substitute bounds to find the area between them. Position, velocity and acceleration are related through anti-derivatives.

Adaptive Parallelization of Queries over Dependent Web Service Calls

Presentation at 1st IEEE Workshop on Information & Software as Services(WISS 2009)Shanghai, China, March 29, 2009

Lecture 4 modelling-of_electrical__electronic_systems (1)

This document provides an overview of mathematical modeling of electrical and electronic systems. It discusses the basic elements of electrical systems including resistors, capacitors, and inductors. It provides the voltage-current relationships and Laplace transforms for each element. Examples are presented on finding transfer functions for circuits containing resistors, capacitors, and inductors. The document also discusses operational amplifiers and provides examples of determining transfer functions for circuits using op-amps, including inverting and non-inverting configurations.

effect of learning rate

The document discusses the learning rate hyperparameter in neural networks. It explains that the learning rate controls how much the model weights are adjusted with respect to the loss gradient at each iteration. It influences the rate of convergence, and choosing the right learning rate is important but challenging as a value that is too small or large can negatively impact training. The document also discusses using momentum to improve the rate of convergence by adding a fraction of the previous weight change to the current change.

Ann a Algorithms notes

Gradient descent is an optimization algorithm used to minimize a cost function by iteratively adjusting parameter values in the direction of the steepest descent. It works by calculating the derivative of the cost function to determine which direction leads to lower cost, then taking a step in that direction. This process repeats until reaching a minimum. Gradient descent is simple but requires knowing the gradient of the cost function. Backpropagation extends gradient descent to neural networks by propagating error backwards from the output to calculate gradients to update weights.

Strategic Oscillation for Exploitation and Exploration of ACS Algorithm for J...

Strategic Oscillation for Exploitation and Exploration of ACS Algorithm for J...University Utara Malaysia

This document summarizes a research paper that proposes a new scheduling algorithm called Strategic Oscillation Ant Colony System (SOACS) for grid computing resource management. SOACS enhances the exploitation and exploration mechanisms in ant colony system by implementing a concept of strategic oscillation between the two. The algorithm was tested on a job scheduling problem and evaluated based on makespan and flowtime metrics. Results showed SOACS outperformed other metaheuristics algorithms on both metrics. Future work is suggested to implement strategic oscillation at the ant level and apply SOACS to other combinatorial problems.Integral method to analyze reaction kinetics

1) There are two methods for analyzing kinetic data - the integral method and the differential method.
2) The integral method involves guessing a rate equation and integrating to predict a linear concentration-time relationship. The differential method analyzes rate directly from concentration-time data without integration.
3) An example reaction is analyzed using both methods. The integral method indicates first-order kinetics while the differential method derives a rate constant.

ControlsLab1

This document describes a lab experiment where students modeled a motor/flywheel system using LabVIEW. They collected data for sinusoidal and square voltage waveforms and compared the experimental model to a theoretical model based on motor specifications. Key aspects of the comparison included transfer functions, step responses, and Bode plots. Students determined parameter values, created VIs to collect experimental data, and analyzed results to compare experimental and theoretical models.

Meeting w3 chapter 2 part 1

This document provides an overview of analog control systems and Laplace transforms. It introduces key concepts like Laplace transforms, common time domain inputs, transfer functions, and modeling electrical, mechanical and electromechanical systems using block diagrams and mathematical models. Examples are provided to illustrate Laplace transforms, transfer functions, and analyzing system response using poles, zeros and stability analysis.

Sensor Fusion Study - Ch3. Least Square Estimation [강소라, Stella, Hayden]

This document discusses Wiener filtering and recursive least squares estimation. It begins with an introduction to Wiener filtering, providing an overview of its history and development. It then discusses how the power spectrum of a stochastic process changes when passed through a linear time-invariant system. Next, it formulates the problem of using a linear filter to extract a signal from additive noise. It derives expressions for the power spectrum of the error and its variance. Finally, it considers optimizing a parametric filter by assuming the optimal filter is a first-order low-pass filter and that the signal and noise spectra are known forms. It derives an expression for the optimal parameter T based on minimizing the error variance.

AR model

This document discusses methods for selecting the order of an autoregressive (AR) model. It explains that AR models depend only on previous outputs and have poles but no zeros. Several criteria for selecting the optimal AR model order are presented, including the Akaike Information Criterion (AIC) and Finite Prediction Error (FPE) criterion. Higher order models fit the data better but can introduce spurious peaks, so the goal is to minimize criteria like AIC or FPE to find the best balance. The document concludes that while these criteria provide guidance, the optimal order depends on the specific data, and inconsistencies can exist between the different methods.

Sensor Fusion Study - Ch15. The Particle Filter [Seoyeon Stella Yang]

The particle filter is a statistical approach to estimation that works well for problems where the Kalman filter fails due to nonlinearities. It approximates the conditional probability distribution of the state using weighted particles. The weights are updated using Bayes' rule based on new measurements. However, particle filters can suffer from sample impoverishment over time, where most particles have negligible weight. Various techniques like roughening, prior editing, and Markov chain Monte Carlo resampling are used to address this issue.

Webinar on Demystifying Data Acquistion Systems: Access Data through Matlab, ...

Webinar on Demystifying Data Acquistion Systems: Access Data through Matlab, ...Manipal Institute of Technology

This document discusses a webinar on demystifying data acquisition and accessing data through LabVIEW, MATLAB, and Simulink. The webinar covers basics of data acquisition systems including sampling rate, aliasing, resolution, range, and normalization. It also discusses various hardware options for data acquisition and interfacing data acquisition devices with software platforms like MATLAB, Simulink, and LabVIEW.Chapter 7

This document discusses methods for detecting autocorrelation in econometrics models. There are two main approaches: graphical methods which involve plotting residuals over time or against lagged residuals, and numerical methods like the Durbin-Watson test and Breusch-Godfrey test. The Durbin-Watson test checks for first-order autocorrelation while the Breusch-Godfrey test allows testing for autocorrelation at different orders. Both tests analyze the residuals from an ordinary least squares regression to determine if autocorrelation is present based on the test statistic value and p-value.

Bioesta

Reaction time experiments were conducted under different initial vehicle velocities and road surface conditions to measure a driver's delay in braking response after seeing brake lights. Ten trials were run for each combination of initial velocity (60 km/hr and 120 km/hr) and frictional coefficient (0.8 for dry conditions and 0.4 for rainy conditions). The delay time and distance traveled before braking were recorded to analyze a person's reaction time under various scenarios.

observer based state feedback controller design in Matlab (Simulink).

In this lecture observer based feedback controller is designed in matlab Simulink
For complete design procedure Watch Video
https://youtu.be/Lax3etc837U

Data mining model

This document discusses Receiver Operating Characteristic (ROC) curves and how they are used to evaluate machine learning models and predictions. It explains that ROC curves plot the true positive rate against the false positive rate for different classification thresholds. The area under the ROC curve (AUC) indicates the model's ability to correctly classify examples, with higher values indicating better performance. The document then provides a Python example to generate ROC curves for random forest and naive Bayes classifiers trained on a synthetic dataset, finding that the naive Bayes model has the best performance with an AUC of 0.993.

Stats computing project_final

The document summarizes a project analyzing factors that impact the landing distance of commercial flights. It includes:
- Combining and cleaning two datasets of 950 flight landings, removing duplicates and abnormal values
- Descriptive analysis finding linear relationships between distance and ground speed, and less linear relationships with other factors
- Linear regression identifying ground speed, height, and aircraft type as significant factors impacting landing distance
- The final model uses 832 observations and explains landing distance based on ground speed, height, and whether the aircraft is an Airbus or Boeing

recursive problem_solving

This document discusses recursive problem solving, where a problem is broken down into smaller instances of the same problem. For a recursive procedure to work, it must have a base case - an input where the solution is already known. The procedure recursively applies itself to progressively smaller inputs until the base case is reached. This evaluation "spirals" inward until the base case output unwinds back to the original call. The example procedure g recursively counts down from its input n to 0 before returning 1.

Lesson 4 ar-ma

This document discusses autocorrelation models and their applications in Python. It describes the autocorrelation function (ACF) and partial autocorrelation function (PACF), and how they are used to identify autoregressive (AR) and moving average (MA) time series models. AR models regress the current value on prior values, while MA models regress the current value on prior noise terms. The document demonstrates how to interpret ACF and PACF plots to select AR or MA models, and how to fit these models in Python.

Applications of derivatives

Derivatives can be used for several applications:
1) Finding increasing or decreasing intervals by taking the derivative and setting it equal to zero.
2) Locating local minima and maxima by taking the derivative, setting it equal to zero, and testing values on either side.
3) Finding absolute minima and maxima on an interval by checking endpoints and local minima/maxima.
4) Determining concavity by taking the second derivative and whether it is positive or negative.
5) Locating points of inflection where the graph changes concavity by setting the second derivative equal to zero.
6) Relating position, velocity, and acceleration through their derivatives.
7

Rank adjustment strategies for Dynamic PageRank : REPORT

This is my report on:
Rank adjustment strategies for Dynamic PageRank (v1).
While doing research work under Prof. Dip Banerjee, Prof. Kishore Kothapalli.
Abstract — To avoid calculating ranks of vertices in a dynamic graph from scratch for every snapshot, the ones computed in the previous snapshot of the graph can be used, with adjustment. Four different rank adjustment strategies for dynamic PageRank are studied here. These include zero-fill, 1/N-fill, scaled zero-fill, and scaled 1/N-fill. Results indicate that the scaled 1/N-fill strategy requires the least number of iterations, on average. As long as the graph has no affected dead ends (including dead ends in the previous snapshot), unaffected vertices can be skipped with this adjustment strategy.
Index terms — PageRank algorithm, Dynamic graph, Rank adjustment, Initial ranks.

Integration

Integrals are used to find the area or volume bounded by lines and can be definite or indefinite. There are several methods for solving integrals including the reverse power rule, substitution method, and evaluating definite integrals by substituting the bounds. Indefinite integrals are anti-derivatives with an added constant, while definite integrals substitute bounds to find the area between them. Position, velocity and acceleration are related through anti-derivatives.

Adaptive Parallelization of Queries over Dependent Web Service Calls

Presentation at 1st IEEE Workshop on Information & Software as Services(WISS 2009)Shanghai, China, March 29, 2009

Lecture 4 modelling-of_electrical__electronic_systems (1)

This document provides an overview of mathematical modeling of electrical and electronic systems. It discusses the basic elements of electrical systems including resistors, capacitors, and inductors. It provides the voltage-current relationships and Laplace transforms for each element. Examples are presented on finding transfer functions for circuits containing resistors, capacitors, and inductors. The document also discusses operational amplifiers and provides examples of determining transfer functions for circuits using op-amps, including inverting and non-inverting configurations.

Meeting w3 chapter 2 part 1

Meeting w3 chapter 2 part 1

Sensor Fusion Study - Ch3. Least Square Estimation [강소라, Stella, Hayden]

Sensor Fusion Study - Ch3. Least Square Estimation [강소라, Stella, Hayden]

AR model

AR model

Sensor Fusion Study - Ch15. The Particle Filter [Seoyeon Stella Yang]

Sensor Fusion Study - Ch15. The Particle Filter [Seoyeon Stella Yang]

Webinar on Demystifying Data Acquistion Systems: Access Data through Matlab, ...

Webinar on Demystifying Data Acquistion Systems: Access Data through Matlab, ...

Chapter 7

Chapter 7

Bioesta

Bioesta

observer based state feedback controller design in Matlab (Simulink).

observer based state feedback controller design in Matlab (Simulink).

Data mining model

Data mining model

Stats computing project_final

Stats computing project_final

recursive problem_solving

recursive problem_solving

Lesson 4 ar-ma

Lesson 4 ar-ma

Applications of derivatives

Applications of derivatives

Rank adjustment strategies for Dynamic PageRank : REPORT

Rank adjustment strategies for Dynamic PageRank : REPORT

Integration

Integration

Adaptive Parallelization of Queries over Dependent Web Service Calls

Adaptive Parallelization of Queries over Dependent Web Service Calls

Lecture 4 modelling-of_electrical__electronic_systems (1)

Lecture 4 modelling-of_electrical__electronic_systems (1)

effect of learning rate

The document discusses the learning rate hyperparameter in neural networks. It explains that the learning rate controls how much the model weights are adjusted with respect to the loss gradient at each iteration. It influences the rate of convergence, and choosing the right learning rate is important but challenging as a value that is too small or large can negatively impact training. The document also discusses using momentum to improve the rate of convergence by adding a fraction of the previous weight change to the current change.

Ann a Algorithms notes

Gradient descent is an optimization algorithm used to minimize a cost function by iteratively adjusting parameter values in the direction of the steepest descent. It works by calculating the derivative of the cost function to determine which direction leads to lower cost, then taking a step in that direction. This process repeats until reaching a minimum. Gradient descent is simple but requires knowing the gradient of the cost function. Backpropagation extends gradient descent to neural networks by propagating error backwards from the output to calculate gradients to update weights.

Strategic Oscillation for Exploitation and Exploration of ACS Algorithm for J...

Strategic Oscillation for Exploitation and Exploration of ACS Algorithm for J...University Utara Malaysia

This document summarizes a research paper that proposes a new scheduling algorithm called Strategic Oscillation Ant Colony System (SOACS) for grid computing resource management. SOACS enhances the exploitation and exploration mechanisms in ant colony system by implementing a concept of strategic oscillation between the two. The algorithm was tested on a job scheduling problem and evaluated based on makespan and flowtime metrics. Results showed SOACS outperformed other metaheuristics algorithms on both metrics. Future work is suggested to implement strategic oscillation at the ant level and apply SOACS to other combinatorial problems.Integral method to analyze reaction kinetics

1) There are two methods for analyzing kinetic data - the integral method and the differential method.
2) The integral method involves guessing a rate equation and integrating to predict a linear concentration-time relationship. The differential method analyzes rate directly from concentration-time data without integration.
3) An example reaction is analyzed using both methods. The integral method indicates first-order kinetics while the differential method derives a rate constant.

ControlsLab1

This document describes a lab experiment where students modeled a motor/flywheel system using LabVIEW. They collected data for sinusoidal and square voltage waveforms and compared the experimental model to a theoretical model based on motor specifications. Key aspects of the comparison included transfer functions, step responses, and Bode plots. Students determined parameter values, created VIs to collect experimental data, and analyzed results to compare experimental and theoretical models.

Adjusting OpenMP PageRank : SHORT REPORT / NOTES

For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).

Python_Module_2.pdf

The document discusses different types of loops in Python including while loops, for loops, and infinite loops. It provides examples of using while loops to iterate until a condition is met, using for loops to iterate over a set of elements when the number of iterations is known, and how to terminate loops early using break or skip iterations using continue. It also discusses using the range() function to generate a sequence of numbers to iterate over in for loops.

Taguchi design of experiments nov 24 2013

This document provides an overview of Taguchi design of experiments. It defines Taguchi methods, which use orthogonal arrays to conduct a minimal number of experiments that can provide full information on factors that affect performance. The assumptions of Taguchi methods include additivity of main effects. The key steps in an experiment are selecting variables and their levels, choosing an orthogonal array, assigning variables to columns, conducting experiments, and analyzing data through sensitivity analysis and ANOVA.

2018867974 sulaim (2)

This document describes using MATLAB to simulate packet traffic using an M/M/1 queue with Erlang C distribution. The simulation finds:
1) The probability of packets being delayed is 0.6402.
2) The average delay for all packets is 382.33 microseconds.
3) The probability of delay exceeding 10 microseconds is 0.6296.
4) The probability of delay exceeding 4 milliseconds is 0.0007895.
The results show the system can acceptably handle packets with over 99% experiencing delays under 4 milliseconds.

Performance measures

The document discusses various performance measures for parallel computing including speedup, efficiency, Amdahl's law, and Gustafson's law. Speedup is defined as the ratio of sequential to parallel execution time. Efficiency is defined as speedup divided by the number of processors. Amdahl's law provides an upper bound on speedup based on the fraction of sequential operations, while Gustafson's law estimates speedup based on the fraction of time spent in serial code for a fixed problem size on varying processors. Other topics covered include performance bottlenecks, data races, data race avoidance techniques, and deadlock avoidance using virtual channels.

Training Deep Networks with Backprop (D1L4 Insight@DCU Machine Learning Works...

Training Deep Networks with Backprop (D1L4 Insight@DCU Machine Learning Works...Universitat Politècnica de Catalunya

https://github.com/telecombcn-dl/dlmm-2017-dcu
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.Chapter 18,19

This document discusses techniques for evaluating and improving statistical models, including regularized regression methods. It covers residuals, Q-Q plots, histograms to evaluate model fit. It also discusses comparing models using ANOVA, AIC, BIC, cross-validation, bootstrapping. Regularization methods like lasso, ridge and elastic net are introduced. Parallel computing is used to more efficiently select hyperparameters for elastic net models.

Why you should use a testing framework

The document discusses why using a testing framework is beneficial. It recommends creating individual test functions, a suite function to run tests, and utility functions. A testing framework would make thorough testing less onerous by providing a standardized way to easily create, run and maintain tests. The document concludes that testing frameworks are a good idea and provides resources for learning more about testing frameworks.

Lecture 7.pptx

The document discusses several algorithms:
1. Program verification - A technique to mathematically prove that a program's output matches its specifications for any input.
2. Algorithm efficiency - Designing algorithms that are economical with resources like time and memory.
3. Analysis of algorithms - Qualities like correctness, understandability and efficiency.
It then provides examples of algorithms for:
1. Swapping variable values
2. Counting elements that meet a condition in a set
3. Summing a set of numbers
4. Computing factorials
5. Evaluating trigonometric functions like sine

Multilayer & Back propagation algorithm

The document discusses multilayer neural networks and the backpropagation algorithm. It begins by introducing sigmoid units as differentiable threshold functions that allow gradient descent to be used. It then describes the backpropagation algorithm, which employs gradient descent to minimize error by adjusting weights. Key aspects covered include defining error terms for multiple outputs, deriving the weight update rules, and generalizing to arbitrary acyclic networks. Issues like local minima and representational power are also addressed.

R analysis of covariance

This presentation educates you about R - Logistic Regression, ANCOVA Analysis and Comparing Two Models with basic syntax.
For more topics stay tuned with Learnbay.

Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.

Compensator Design for Speed Control of DC Motor by Root Locus Approach using...

The document discusses designing a compensator for speed control of a DC motor using the root locus approach in MATLAB. It first presents the problem statement of controlling the speed of a DC motor and introduces using a compensator as a controller. It then provides the design procedure for three types of compensators: lead compensator, lag compensator, and lag-lead compensator. The procedures include calculating transfer functions, plotting root loci, and determining pole and zero locations. Flow charts of the MATLAB program for each compensator type are presented. Finally, the results of applying each compensator are shown and their effects on performance parameters like settling time are compared.

Chapter 10-pid-1

Here are the steps to solve this problem:
1) The open loop transfer function is given as:
G(s) = Kp/(Ts+1)
Where T = 1 sec, Kp = 1
2) To reduce overshoot, we need to add a derivative term (lead compensation)
3) A lead compensator is of the form:
Gc(s) = (s+z)/(s+p)
Where z < p
4) Try z = 2, p = 10
5) The closed loop transfer function is:
Gcl(s) = KpGc(s)/(Ts+1+KpGc(s))
= (s+2

(Slides) Efficient Evaluation Methods of Elementary Functions Suitable for SI...

The document proposes efficient methods for evaluating elementary functions like sin, cos, tan, log, and exp using SIMD instructions. The methods are twice as fast as floating point unit evaluation and have a maximum error of 6 ulps. They avoid conditional branches, gathering/scattering operations, and table lookups. Trigonometric functions are evaluated in two steps - argument reduction followed by a series evaluation. Inverse trigonometric, exponential and logarithmic functions are also efficiently evaluated in a similar manner suitable for SIMD computation. Evaluation accuracy and speed are evaluated against existing methods and the code size is kept small.

effect of learning rate

effect of learning rate

Ann a Algorithms notes

Ann a Algorithms notes

Strategic Oscillation for Exploitation and Exploration of ACS Algorithm for J...

Strategic Oscillation for Exploitation and Exploration of ACS Algorithm for J...

Integral method to analyze reaction kinetics

Integral method to analyze reaction kinetics

ControlsLab1

ControlsLab1

Adjusting OpenMP PageRank : SHORT REPORT / NOTES

Adjusting OpenMP PageRank : SHORT REPORT / NOTES

Python_Module_2.pdf

Python_Module_2.pdf

Taguchi design of experiments nov 24 2013

Taguchi design of experiments nov 24 2013

2018867974 sulaim (2)

2018867974 sulaim (2)

Performance measures

Performance measures

Training Deep Networks with Backprop (D1L4 Insight@DCU Machine Learning Works...

Training Deep Networks with Backprop (D1L4 Insight@DCU Machine Learning Works...

Chapter 18,19

Chapter 18,19

Why you should use a testing framework

Why you should use a testing framework

Lecture 7.pptx

Lecture 7.pptx

Multilayer & Back propagation algorithm

Multilayer & Back propagation algorithm

R analysis of covariance

R analysis of covariance

Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Algorithmic optimizations for Dynamic Levelwise PageRank (from STICD) : SHORT...

Compensator Design for Speed Control of DC Motor by Root Locus Approach using...

Compensator Design for Speed Control of DC Motor by Root Locus Approach using...

Chapter 10-pid-1

Chapter 10-pid-1

(Slides) Efficient Evaluation Methods of Elementary Functions Suitable for SI...

(Slides) Efficient Evaluation Methods of Elementary Functions Suitable for SI...

About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...

TrueTime is a service that enables the use of globally synchronized clocks, with bounded error. It returns a time interval that is guaranteed to contain the clock’s actual time for some time during the call’s execution. If two intervals do not overlap, then we know calls were definitely ordered in real time. In general, synchronized clocks can be used to avoid communication in a distributed system.
The underlying source of time is a combination of GPS receivers and atomic clocks. As there are “time masters” in every datacenter (redundantly), it is likely that both sides of a partition would continue to enjoy accurate time. Individual nodes however need network connectivity to the masters, and without it their clocks will drift. Thus, during a partition their intervals slowly grow wider over time, based on bounds on the rate of local clock drift. Operations depending on TrueTime, such as Paxos leader election or transaction commits, thus have to wait a little longer, but the operation still completes (assuming the 2PC and quorum communication are working).

Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.

Adjusting Bitset for graph : SHORT REPORT / NOTES

Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is commonly used for efficient graph computations. Unfortunately, using CSR for dynamic graphs is impractical since addition/deletion of a single edge can require on average (N+M)/2 memory accesses, in order to update source-offsets and destination-indices. A common approach is therefore to store edge-lists/destination-indices as an array of arrays, where each edge-list is an array belonging to a vertex. While this is good enough for small graphs, it quickly becomes a bottleneck for large graphs. What causes this bottleneck depends on whether the edge-lists are sorted or unsorted. If they are sorted, checking for an edge requires about log(E) memory accesses, but adding an edge on average requires E/2 accesses, where E is the number of edges of a given vertex. Note that both addition and deletion of edges in a dynamic graph require checking for an existing edge, before adding or deleting it. If edge lists are unsorted, checking for an edge requires around E/2 memory accesses, but adding an edge requires only 1 memory access.

Adjusting primitives for graph : SHORT REPORT / NOTES

Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).

Experiments with Primitive operations : SHORT REPORT / NOTES

This includes:
- Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
- Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
- Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
- Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).

PageRank Experiments : SHORT REPORT / NOTES

This includes:
- Adjusting data types for rank vector
- Adjusting Pagerank parameters
- Adjusting Sequential approach
- Adjusting OpenMP approach
- Comparing sequential approach
- Adjusting Monolithic (Sequential) optimizations (from STICD)
- Adjusting Levelwise (STICD) approach
- Comparing Levelwise (STICD) approach
- Adjusting ranks for dynamic graphs
- Adjusting Levelwise (STICD) dynamic approach
- Comparing dynamic approach with static
- Adjusting Monolithic CUDA approach
- Adjusting Monolithic CUDA optimizations (from STICD)
- Adjusting Levelwise (STICD) CUDA approach
- Comparing Levelwise (STICD) CUDA approach
- Comparing dynamic CUDA approach with static
- Comparing dynamic optimized CUDA approach with static

Algorithmic optimizations for Dynamic Monolithic PageRank (from STICD) : SHOR...

Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.

word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...

Below are the important points I note from the 2020 paper by Martin Grohe:
- 1-WL distinguishes almost all graphs, in a probabilistic sense
- Classical WL is two dimensional Weisfeiler-Leman
- DeepWL is an unlimited version of WL graph that runs in polynomial time.
- Knowledge graphs are essentially graphs with vertex/edge attributes
ABSTRACT:
Vector representations of graphs and relational structures, whether handcrafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures. A wide range of methods for generating such embeddings have been studied in the machine learning and knowledge representation literature. However, vector embeddings have received relatively little attention from a theoretical point of view.
Starting with a survey of embedding techniques that have been used in practice, in this paper we propose two theoretical approaches that we see as central for understanding the foundations of vector embeddings. We draw connections between the various approaches and suggest directions for future research.

DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES

https://gist.github.com/wolfram77/54c4a14d9ea547183c6c7b3518bf9cd1
There exist a number of dynamic graph generators. Barbasi-Albert model iteratively attach new vertices to pre-exsiting vertices in the graph using preferential attachment (edges to high degree vertices are more likely - rich get richer - Pareto principle). However, graph size increases monotonically, and density of graph keeps increasing (sparsity decreasing).
Gorke's model uses a defined clustering to uniformly add vertices and edges. Purohit's model uses motifs (eg. triangles) to mimick properties of existing dynamic graphs, such as growth rate, structure, and degree distribution. Kronecker graph generators are used to increase size of a given graph, with power-law distribution.
To generate dynamic graphs, we must choose a metric to compare two graphs. Common metrics include diameter, clustering coefficient (modularity?), triangle counting (triangle density?), and degree distribution.
In this paper, the authors propose Dygraph, a dynamic graph generator that uses degree distribution as the only metric. The authors observe that many real-world graphs differ from the power-law distribution at the tail end. To address this issue, they propose binning, where the vertices beyond a certain degree (minDeg = min(deg) s.t. |V(deg)| < H, where H~10 is the number of vertices with a given degree below which are binned) are grouped into bins of degree-width binWidth, max-degree localMax, and number of degrees in bin with at least one vertex binSize (to keep track of sparsity). This helps the authors to generate graphs with a more realistic degree distribution.
The process of generating a dynamic graph is as follows. First the difference between the desired and the current degree distribution is calculated. The authors then create an edge-addition set where each vertex is present as many times as the number of additional incident edges it must recieve. Edges are then created by connecting two vertices randomly from this set, and removing both from the set once connected. Currently, authors reject self-loops and duplicate edges. Removal of edges is done in a similar fashion.
Authors observe that adding edges with power-law properties dominates the execution time, and consider parallelizing DyGraph as part of future work.

Shared memory Parallelism (NOTES)

My notes on shared memory parallelism.
Shared memory is memory that may be simultaneously accessed by multiple programs with an intent to provide communication among them or avoid redundant copies. Shared memory is an efficient means of passing data between programs. Using memory for communication inside a single program, e.g. among its multiple threads, is also referred to as shared memory [REF].

A Dynamic Algorithm for Local Community Detection in Graphs : NOTES

**Community detection methods** can be *global* or *local*. **Global community detection methods** divide the entire graph into groups. Existing global algorithms include:
- Random walk methods
- Spectral partitioning
- Label propagation
- Greedy agglomerative and divisive algorithms
- Clique percolation
https://gist.github.com/wolfram77/b4316609265b5b9f88027bbc491f80b6
There is a growing body of work in *detecting overlapping communities*. **Seed set expansion** is a **local community detection method** where a relevant *seed vertices* of interest are picked and *expanded to form communities* surrounding them. The quality of each community is measured using a *fitness function*.
**Modularity** is a *fitness function* which compares the number of intra-community edges to the expected number in a random-null model. **Conductance** is another popular fitness score that measures the community cut or inter-community edges. Many *overlapping community detection* methods **use a modified ratio** of intra-community edges to all edges with atleast one endpoint in the community.
Andersen et al. use a **Spectral PageRank-Nibble method** which minimizes conductance and is formed by adding vertices in order of decreasing PageRank values. Andersen and Lang develop a **random walk approach** in which some vertices in the seed set may not be placed in the final community. Clauset gives a **greedy method** that *starts from a single vertex* and then iteratively adds neighboring vertices *maximizing the local modularity score*. Riedy et al. **expand multiple vertices** via maximizing modularity.
Several algorithms for **detecting global, overlapping communities** use a *greedy*, *agglomerative approach* and run *multiple separate seed set expansions*. Lancichinetti et al. run **greedy seed set expansions**, each with a *single seed vertex*. Overlapping communities are produced by a sequentially running expansions from a node not yet in a community. Lee et al. use **maximal cliques as seed sets**. Havemann et al. **greedily expand cliques**.
The authors of this paper discuss a dynamic approach for **community detection using seed set expansion**. Simply marking the neighbours of changed vertices is a **naive approach**, and has *severe shortcomings*. This is because *communities can split apart*. The simple updating method *may fail even when it outputs a valid community* in the graph.

Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

A **community** (in a network) is a subset of nodes which are _strongly connected among themselves_, but _weakly connected to others_. Neither the number of output communities nor their size distribution is known a priori. Community detection methods can be divisive or agglomerative. **Divisive methods** use _betweeness centrality_ to **identify and remove bridges** between communities. **Agglomerative methods** greedily **merge two communities** that provide maximum gain in _modularity_. Newman and Girvan have introduced the **modularity metric**. The problem of community detection is then reduced to the problem of modularity maximization which is **NP-complete**. **Louvain method** is a variant of the _agglomerative strategy_, in that is a _multi-level heuristic_.
https://gist.github.com/wolfram77/917a1a4a429e89a0f2a1911cea56314d
In this paper, the authors discuss **four heuristics** for Community detection using the _Louvain algorithm_ implemented upon recently developed **Grappolo**, which is a parallel variant of the Louvain algorithm. They are:
- Vertex following and Minimum label
- Data caching
- Graph coloring
- Threshold scaling
With the **Vertex following** heuristic, the _input is preprocessed_ and all single-degree vertices are merged with their corresponding neighbours. This helps reduce the number of vertices considered in each iteration, and also help initial seeds of communities to be formed. With the **Minimum label heuristic**, when a vertex is making the decision to move to a community and multiple communities provided the same modularity gain, the community with the smallest id is chosen. This helps _minimize or prevent community swaps_. With the **Data caching** heuristic, community information is stored in a vector instead of a map, and is reused in each iteration, but with some additional cost. With the **Vertex ordering via Graph coloring** heuristic, _distance-k coloring_ of graphs is performed in order to group vertices into colors. Then, each set of vertices (by color) is processed _concurrently_, and synchronization is performed after that. This enables us to mimic the behaviour of the serial algorithm. Finally, with the **Threshold scaling** heuristic, _successively smaller values of modularity threshold_ are used as the algorithm progresses. This allows the algorithm to converge faster, and it has been observed a good modularity score as well.
From the results, it appears that _graph coloring_ and _threshold scaling_ heuristics do not always provide a speedup and this depends upon the nature of the graph. It would be interesting to compare the heuristics against baseline approaches. Future work can include _distributed memory implementations_, and _community detection on streaming graphs_.

Application Areas of Community Detection: A Review : NOTES

This is a short review of Community detection methods (on graphs), and their applications. A **community** is a subset of a network whose members are *highly connected*, but *loosely connected* to others outside their community. Different community detection methods *can return differing communities* these algorithms are **heuristic-based**. **Dynamic community detection** involves tracking the *evolution of community structure* over time.
https://gist.github.com/wolfram77/09e64d6ba3ef080db5558feb2d32fdc0
Communities can be of the following **types**:
- Disjoint
- Overlapping
- Hierarchical
- Local.
The following **static** community detection **methods** exist:
- Spectral-based
- Statistical inference
- Optimization
- Dynamics-based
The following **dynamic** community detection **methods** exist:
- Independent community detection and matching
- Dependent community detection (evolutionary)
- Simultaneous community detection on all snapshots
- Dynamic community detection on temporal networks
**Applications** of community detection include:
- Criminal identification
- Fraud detection
- Criminal activities detection
- Bot detection
- Dynamics of epidemic spreading (dynamic)
- Cancer/tumor detection
- Tissue/organ detection
- Evolution of influence (dynamic)
- Astroturfing
- Customer segmentation
- Recommendation systems
- Social network analysis (both)
- Network summarization
- Privary, group segmentation
- Link prediction (both)
- Community evolution prediction (dynamic, hot field)
<br>
<br>
## References
- [Application Areas of Community Detection: A Review : PAPER](https://ieeexplore.ieee.org/document/8625349)

Community Detection on the GPU : NOTES

This paper discusses a GPU implementation of the Louvain community detection algorithm. Louvain algorithm obtains hierachical communities as a dendrogram through modularity optimization. Given an undirected weighted graph, all vertices are first considered to be their own communities. In the first phase, each vertex greedily decides to move to the community of one of its neighbours which gives greatest increase in modularity. If moving to no neighbour's community leads to an increase in modularity, the vertex chooses to stay with its own community. This is done sequentially for all the vertices. If the total change in modularity is more than a certain threshold, this phase is repeated. Once this local moving phase is complete, all vertices have formed their first hierarchy of communities. The next phase is called the aggregation phase, where all the vertices belonging to a community are collapsed into a single super-vertex, such that edges between communities are represented as edges between respective super-vertices (edge weights are combined), and edges within each community are represented as self-loops in respective super-vertices (again, edge weights are combined). Together, the local moving and the aggregation phases constitute a stage. This super-vertex graph is then used as input fof the next stage. This process continues until the increase in modularity is below a certain threshold. As a result from each stage, we have a hierarchy of community memberships for each vertex as a dendrogram.
Approaches to perform the Louvain algorithm can be divided into coarse-grained and fine-grained. Coarse-grained approaches process a set of vertices in parallel, while fine-grained approaches process all vertices in parallel. A coarse-grained hybrid-GPU algorithm using multi GPUs has be implemented by Cheong et al. which grabbed my attention. In addition, their algorithm does not use hashing for the local moving phase, but instead sorts each neighbour list based on the community id of each vertex.
https://gist.github.com/wolfram77/7e72c9b8c18c18ab908ae76262099329

Survey for extra-child-process package : NOTES

Useful additions to inbuilt child_process module.
📦 Node.js, 📜 Files, 📰 Docs.
Please see attached PDF for literature survey.
https://gist.github.com/wolfram77/d936da570d7bf73f95d1513d4368573e

Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

This paper presents two algorithms for efficiently computing PageRank on dynamically updating graphs in a batched manner: DynamicLevelwisePR and DynamicMonolithicPR. DynamicLevelwisePR processes vertices level-by-level based on strongly connected components and avoids recomputing converged vertices on the CPU. DynamicMonolithicPR uses a full power iteration approach on the GPU that partitions vertices by in-degree and skips unaffected vertices. Evaluation on real-world graphs shows the batched algorithms provide speedups of up to 4000x over single-edge updates and outperform other state-of-the-art dynamic PageRank algorithms.

Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

For the PhD forum an abstract submission is required by 10th May, and poster by 15th May. The event is on 30th May.
https://gist.github.com/wolfram77/1c1f730d20b51e0d2c6d477fd3713024

Fast Incremental Community Detection on Dynamic Graphs : NOTES

In this paper, the authors describe two approaches for dynamic community detection using the CNM algorithm. CNM is a hierarchical, agglomerative algorithm that greedily maximizes modularity. They define two approaches: BasicDyn and FastDyn. BasicDyn backtracks merges of communities until each marked (changed) vertex is its own singleton community. FastDyn undoes a merge only if the quality of merge, as measured by the induced change in modularity, has significantly decreased compared to when the merge initially took place. FastDyn also allows more than two vertices to contract together if in the previous time step these vertices eventually ended up contracted in the same community. In the static case, merging several vertices together in one contraction phase could lead to deteriorating results. FastDyn is able to do this, however, because it uses information from the merges of the previous time step. Intuitively, merges that previously occurred are more likely to be acceptable later.
https://gist.github.com/wolfram77/1856b108334cc822cdddfdfa7334792a

Can you ﬁx farming by going back 8000 years : NOTES

1. Human population didn't explode, but plateued.
2. Fertilizer prices are going to the sky.
3. Farmers are looking for alternatives such as animal waste (manure) or even human waste.
4. Manure prices are also going up.
5. Switching to organic farming not an option.
https://gist.github.com/wolfram77/49067fc3ddc1ba2e1db4f873056fd88a

HITS algorithm : NOTES

1. Webpages tend to behave as authorities or hubs.
2. An authority represents an research thesis, and a hub represents an encyclopedia.
3. Each page has an authority and a hub score.
4. The graph is based on query, included pointed to and from pages.
5. Authority score is the sum of scores of all hubs pointing to it.
6. Hub score is the sum of scores of all authorities is pointing to.
7. Score are normalized with L2-norm in each iteration (root of sum of squares).
8. Needs to be performed at query time.
9. Two scores are returned, instead of just one.
https://gist.github.com/wolfram77/3d9ef6c5a5b63f53caabce4812c7ea81

About TrueTime, Spanner, Clock synchronization, CAP theorem, Two-phase lockin...

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Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...

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Adjusting Bitset for graph : SHORT REPORT / NOTES

Adjusting Bitset for graph : SHORT REPORT / NOTES

Adjusting primitives for graph : SHORT REPORT / NOTES

Adjusting primitives for graph : SHORT REPORT / NOTES

Experiments with Primitive operations : SHORT REPORT / NOTES

Experiments with Primitive operations : SHORT REPORT / NOTES

PageRank Experiments : SHORT REPORT / NOTES

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Algorithmic optimizations for Dynamic Monolithic PageRank (from STICD) : SHOR...

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word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...

word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings o...

DyGraph: A Dynamic Graph Generator and Benchmark Suite : NOTES

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Shared memory Parallelism (NOTES)

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A Dynamic Algorithm for Local Community Detection in Graphs : NOTES

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Scalable Static and Dynamic Community Detection Using Grappolo : NOTES

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Application Areas of Community Detection: A Review : NOTES

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Community Detection on the GPU : NOTES

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Survey for extra-child-process package : NOTES

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Dynamic Batch Parallel Algorithms for Updating PageRank : POSTER

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Abstract for IPDPS 2022 PhD Forum on Dynamic Batch Parallel Algorithms for Up...

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Fast Incremental Community Detection on Dynamic Graphs : NOTES

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Can you ﬁx farming by going back 8000 years : NOTES

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HITS algorithm : NOTES

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Measures in SQL (SIGMOD 2024, Santiago, Chile)

SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374

Unveiling the Advantages of Agile Software Development.pdf

Learn about Agile Software Development's advantages. Simplify your workflow to spur quicker innovation. Jump right in! We have also discussed the advantages.

WWDC 2024 Keynote Review: For CocoaCoders Austin

Overview of WWDC 2024 Keynote Address.
Covers: Apple Intelligence, iOS18, macOS Sequoia, iPadOS, watchOS, visionOS, and Apple TV+.
Understandable dialogue on Apple TV+
On-device app controlling AI.
Access to ChatGPT with a guest appearance by Chief Data Thief Sam Altman!
App Locking! iPhone Mirroring! And a Calculator!!

Artificia Intellicence and XPath Extension Functions

The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.

UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions

The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.

UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem

Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0

Need for Speed: Removing speed bumps from your Symfony projects ⚡️

No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.

How to write a program in any programming language

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ALGIT - Assembly Line for Green IT - Numbers, Data, Facts

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Modelling Up - DDDEurope 2024 - Amsterdam

What to do when you have a perfect model for your software but you are constrained by an imperfect business model?
This talk explores the challenges of bringing modelling rigour to the business and strategy levels, and talking to your non-technical counterparts in the process.

Fundamentals of Programming and Language Processors

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UI5con 2024 - Bring Your Own Design System

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Enums On Steroids - let's look at sealed classes !

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Webinar On-Demand: Using Flutter for Embedded

Flutter is a popular open source, cross-platform framework developed by Google. In this webinar we'll explore Flutter and its architecture, delve into the Flutter Embedder and Flutter’s Dart language, discover how to leverage Flutter for embedded device development, learn about Automotive Grade Linux (AGL) and its consortium and understand the rationale behind AGL's choice of Flutter for next-gen IVI systems. Don’t miss this opportunity to discover whether Flutter is right for your project.

一比一原版(USF毕业证)旧金山大学毕业证如何办理

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#一整套旧金山大学文凭证件办理#—包含旧金山大学旧金山大学本科毕业证成绩单学历认证|使馆认证|归国人员证明|教育部认证|留信网认证永远存档教育部学历学位认证查询办理国外文凭国外学历学位认证#我们提供全套办理服务。
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Using Query Store in Azure PostgreSQL to Understand Query Performance

Microsoft has added an excellent new extension in PostgreSQL on their Azure Platform. This session, presented at Posette 2024, covers what Query Store is and the types of information you can get out of it.

在线购买加拿大英属哥伦比亚大学毕业证本科学位证书原版一模一样

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Microservice Teams - How the cloud changes the way we work

A lot of technical challenges and complexity come with building a cloud-native and distributed architecture. The way we develop backend software has fundamentally changed in the last ten years. Managing a microservices architecture demands a lot of us to ensure observability and operational resiliency. But did you also change the way you run your development teams?
Sven will talk about Atlassian’s journey from a monolith to a multi-tenanted architecture and how it affected the way the engineering teams work. You will learn how we shifted to service ownership, moved to more autonomous teams (and its challenges), and established platform and enablement teams.

SMS API Integration in Saudi Arabia| Best SMS API Service

Discover the benefits and implementation of SMS API integration in the UAE and Middle East. This comprehensive guide covers the importance of SMS messaging APIs, the advantages of bulk SMS APIs, and real-world case studies. Learn how CEQUENS, a leader in communication solutions, can help your business enhance customer engagement and streamline operations with innovative CPaaS, reliable SMS APIs, and omnichannel solutions, including WhatsApp Business. Perfect for businesses seeking to optimize their communication strategies in the digital age.

Measures in SQL (SIGMOD 2024, Santiago, Chile)

Measures in SQL (SIGMOD 2024, Santiago, Chile)

Unveiling the Advantages of Agile Software Development.pdf

Unveiling the Advantages of Agile Software Development.pdf

WWDC 2024 Keynote Review: For CocoaCoders Austin

WWDC 2024 Keynote Review: For CocoaCoders Austin

Artificia Intellicence and XPath Extension Functions

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UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions

UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions

UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem

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Need for Speed: Removing speed bumps from your Symfony projects ⚡️

Need for Speed: Removing speed bumps from your Symfony projects ⚡️

How to write a program in any programming language

How to write a program in any programming language

ALGIT - Assembly Line for Green IT - Numbers, Data, Facts

ALGIT - Assembly Line for Green IT - Numbers, Data, Facts

Modelling Up - DDDEurope 2024 - Amsterdam

Modelling Up - DDDEurope 2024 - Amsterdam

Fundamentals of Programming and Language Processors

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UI5con 2024 - Bring Your Own Design System

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Enums On Steroids - let's look at sealed classes !

Enums On Steroids - let's look at sealed classes !

Webinar On-Demand: Using Flutter for Embedded

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Lecture 2 - software testing SE 412.pptx

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一比一原版(USF毕业证)旧金山大学毕业证如何办理

一比一原版(USF毕业证)旧金山大学毕业证如何办理

Using Query Store in Azure PostgreSQL to Understand Query Performance

Using Query Store in Azure PostgreSQL to Understand Query Performance

在线购买加拿大英属哥伦比亚大学毕业证本科学位证书原版一模一样

在线购买加拿大英属哥伦比亚大学毕业证本科学位证书原版一模一样

Microservice Teams - How the cloud changes the way we work

Microservice Teams - How the cloud changes the way we work

SMS API Integration in Saudi Arabia| Best SMS API Service

SMS API Integration in Saudi Arabia| Best SMS API Service

- 1. 1 Effect of stepwise adjustment of Damping factor upon PageRank Subhajit Sahu1 , Kishore Kothapalli1 , Dip Sankar Banerjee2 1 International Institute of Information Technology, Hyderabad 2 Indian Institute of Technology, Jodhpur Abstract — The effect of adjusting damping factor α, from a small initial value α0 to the final desired αf value, upon then iterations needed for PageRank computation is observed. Adjustment of the damping factor is done in one or more steps. Results show no improvement in performance over a fixed damping factor based PageRank. Index terms — PageRank algorithm, Step-wise adjustment, Damping factor. 1. Introduction Web graphs are by default reducible, and thus the convergence rate of the power-iteration method is the rate at which αk → 0, where α is the damping factor, and k is the iteration count. An estimate of the number of iterations needed to converge to a tolerance τ is log10 τ / log10 α [1]. For τ = 10-6 and α = 0.85, it can take roughly 85 iterations to converge. For α = 0.95, and α = 0.75, with the same tolerance τ = 10-6 , it takes roughly 269 and 48 iterations respectively. For τ = 10-9 , and τ = 10-3 , with the same damping factor α = 0.85, it takes roughly 128 and 43 iterations respectively. Thus, adjusting the damping factor or the tolerance parameters of the PageRank algorithm can have a significant effect on the convergence rate. 2. Method The idea behind this experiment was to adjust the damping factor α in steps, to see if it might help reduce PageRank computation time. The PageRank computation first starts with a small initial damping factor α = α0. After the
- 2. 2 ranks have converged, the damping factor α is updated to the next damping factor step, say α1 and PageRank computation is continued again. This is done until the final desired value of αf is reached. For example, the computation starts initially with α = α0 = 0.5, lets ranks converge quickly, and then switches to α = αf = 0.85 and continues PageRank computation until it converges. This single-step change is attempted with the initial (fast converge) damping factor α0 from 0.1 to 0.8. Similar to this, two-step, three-step, and four-step changes are also attempted. With a two-step approach, a midpoint between the initial damping value α0 and αf = 0.85 is selected as well for the second set of iterations. Similarly, three-step and four-step approaches use two and three midpoints respectively. 3. Experimental setup A small sample graph is used in this experiment, which is stored in the MatrixMarket (.mtx) file format. The experiment is implemented in Node.js, and executed on a personal laptop. Only the iteration count of each test case is measured. The tolerance τ = 10-5 is used for all test cases. Statistics of each test case is printed to standard output (stdout), and redirected to a log file, which is then processed with a script to generate a CSV file, with each row representing the details of a single test case. This CSV file is imported into Google Sheets, and necessary tables are set up with the help of the FILTER function to create the charts. 4. Results From the results, as shown in figure 4.1, it is clear that modifying the damping factor α in steps is not a good idea. The standard fixed damping factor PageRank, with α = 0.85, converges in 35 iterations. Using a single-step approach increases the total number of iterations required, by at least 4 iterations (with an initial damping factor α0 = 0.1). Increasing α0 further increases the total number of iterations needed for computation. Switching to a multi-step approach also increases the number of iterations needed for convergence. The two-step, three-step, and four-step approaches
- 3. 3 require a total of atleast 49, 60, and 71 iterations respectively. Again, increasing α0 continues to increase the total number of iterations needed for computation. A possible explanation for this effect is that the ranks for different values of the damping factor α are significantly different, and switching to a different damping factor α after each step mostly leads to recomputation. Figure 4.1: Iterations required for PageRank computation, when damping factor α is adjusted in 1-4 steps, starting with an initial small damping factor α0 (damping_start). 0-step is the fixed damping factor PageRank, with α = 0.85. 5. Conclusion Adjusting the damping factor α in steps is not a good idea, most likely because ranks obtained for even nearby damping factors are significantly different. Fixed damping factor PageRank continues to be the best approach. The links to source code, along with data sheets and charts, for adjusting damping factor in steps [2] is included in references.
- 4. 4 References [1] A. Langville and C. Meyer, “Deeper Inside PageRank,” Internet Math., vol. 1, no. 3, pp. 335–380, Jan. 2004, doi: 10.1080/15427951.2004.10129091. [2] S. Sahu, “puzzlef/pagerank-adjust-damping-factor-stepwise.js: Experimenting PageRank improvement by adjusting damping factor (α) between iterations.” https://github.com/puzzlef/pagerank-adjust-damping-factor-stepwise.js (accessed Aug. 06, 2021).