Slides Επιστήμης Δικτύων για υπολογισμούς με την Python στα πλαίσια του μεταπτυχιακού μαθήματος των Ψηφιακών Τεχνολογιών στην Εκπαίδευση του Μαθηματικού Τμήματος του Πανεπιστημίου Πατρών κατά το χειμερινό εξάμηνο 2014-5.
Τα slides αυτά θα γίνονται συνεχώς updated ως το τέλος του εξαμήνου (τέλη Δεκεμβρίου 2014). Η ημερομηνία του update γράφεται στην πρώτη σελίδα των slides.
This document provides an introduction to the Python programming language. It covers basic Python concepts like data types, strings, lists, dictionaries, tuples, date and time functions, conditional statements, loops, and functions. Each concept is explained through examples of Python code. The document is intended as a tutorial for learning the essential elements of the Python syntax and how to write simple Python programs.
Some R Examples[R table and Graphics] -Advanced Data Visualization in R (Some...Dr. Volkan OBAN
Some R Examples[R table and Graphics]
Advanced Data Visualization in R (Some Examples)
References:
http://zevross.com/blog/2014/08/04/beautiful-plotting-in-r-a-ggplot2-cheatsheet-3/
http://www.cookbook-r.com/
http://moderndata.plot.ly/trisurf-plots-in-r-using-plotly/
I hope that it would ne useful for UseRs.
Umarım; R programı ile ilgilenen herkes için yararlı olur.
Volkan OBAN
Advanced Data Visualization in R- Somes Examples.Dr. Volkan OBAN
This document provides examples of using the geomorph package in R for advanced data visualization. It includes code snippets showing how to visualize geometric morphometric data using functions like plotspec() and plotRefToTarget(). It also includes an example of creating a customized violin plot function for comparing multiple groups and generating simulated data to plot.
The document discusses Python lists and basic operations on lists like accessing elements, slicing lists, appending to lists, and using list comprehensions. It provides examples of accessing specific elements of a list x, slicing parts of x into new lists, getting the last element of x, appending a new element to x, and using a list comprehension to multiply each element of x by 10. It also briefly mentions Python control flow statements like if, for, and def as well as common data types like lists, tuples, and dictionaries.
The document discusses various methods for reading data from files in Python, including reading the entire file content into a string, reading the file in chunks, reading one line at a time, and reading all lines at once into a list. It demonstrates counting the total number of characters or getting the average line length for a sample haiku file using different reading methods.
Slides Επιστήμης Δικτύων για υπολογισμούς με την Python στα πλαίσια του μεταπτυχιακού μαθήματος των Ψηφιακών Τεχνολογιών στην Εκπαίδευση του Μαθηματικού Τμήματος του Πανεπιστημίου Πατρών κατά το χειμερινό εξάμηνο 2014-5.
Τα slides αυτά θα γίνονται συνεχώς updated ως το τέλος του εξαμήνου (τέλη Δεκεμβρίου 2014). Η ημερομηνία του update γράφεται στην πρώτη σελίδα των slides.
This document provides an introduction to the Python programming language. It covers basic Python concepts like data types, strings, lists, dictionaries, tuples, date and time functions, conditional statements, loops, and functions. Each concept is explained through examples of Python code. The document is intended as a tutorial for learning the essential elements of the Python syntax and how to write simple Python programs.
Some R Examples[R table and Graphics] -Advanced Data Visualization in R (Some...Dr. Volkan OBAN
Some R Examples[R table and Graphics]
Advanced Data Visualization in R (Some Examples)
References:
http://zevross.com/blog/2014/08/04/beautiful-plotting-in-r-a-ggplot2-cheatsheet-3/
http://www.cookbook-r.com/
http://moderndata.plot.ly/trisurf-plots-in-r-using-plotly/
I hope that it would ne useful for UseRs.
Umarım; R programı ile ilgilenen herkes için yararlı olur.
Volkan OBAN
Advanced Data Visualization in R- Somes Examples.Dr. Volkan OBAN
This document provides examples of using the geomorph package in R for advanced data visualization. It includes code snippets showing how to visualize geometric morphometric data using functions like plotspec() and plotRefToTarget(). It also includes an example of creating a customized violin plot function for comparing multiple groups and generating simulated data to plot.
The document discusses Python lists and basic operations on lists like accessing elements, slicing lists, appending to lists, and using list comprehensions. It provides examples of accessing specific elements of a list x, slicing parts of x into new lists, getting the last element of x, appending a new element to x, and using a list comprehension to multiply each element of x by 10. It also briefly mentions Python control flow statements like if, for, and def as well as common data types like lists, tuples, and dictionaries.
The document discusses various methods for reading data from files in Python, including reading the entire file content into a string, reading the file in chunks, reading one line at a time, and reading all lines at once into a list. It demonstrates counting the total number of characters or getting the average line length for a sample haiku file using different reading methods.
The document introduces Python programming language. It provides an overview of Python's history and key features such as being an interpreted, object-oriented, and platform independent language. It also discusses Python syntax including data types, variables, input/output, operators, conditional statements, loops, functions, and data structures like lists, tuples, dictionaries. Several examples are given to illustrate different Python concepts and syntax.
Diving into byte code optimization in python Chetan Giridhar
The document discusses byte-code optimization in Python. It begins by explaining that Python source code is compiled into byte code, which is then executed by the CPython interpreter. It describes some of the key steps in the compilation process, including parsing the source code and generating an abstract syntax tree (AST) before compiling to bytecodes. The document then discusses some approaches for optimizing Python code at the byte-code level, including using tools like Pyrex, Psyco and the Python bytecode manipulation library BytePlay. It recommends always profiling applications to identify optimization opportunities and considering writing performance-critical portions as C extensions.
The document provides an overview of the Go programming language, including its history, data types, basic syntax like variables and functions, and common constructs like arrays, slices, maps, and concurrency features. It was developed at Google in 2009 and aims to provide efficiency of static typing with ease of dynamic languages through features like garbage collection and good support for concurrency and communication.
Byterun, a Python bytecode interpreter - Allison Kaptur at NYCPythonakaptur
This document discusses Byterun, a Python interpreter written in Python. It explains how Python code is compiled to bytecode which is then interpreted. Key points made include:
- Python code is first compiled to bytecode, which is a sequence of bytes representing operations and arguments.
- The dis module can disassemble bytecode back into a human-readable format showing the instructions.
- The interpreter works by reading each bytecode instruction and carrying out the corresponding operation, such as loading variables or performing arithmetic.
- This dynamic execution allows Python to behave differently based on the types of values at runtime, like formatting strings.
Allison Kaptur: Bytes in the Machine: Inside the CPython interpreter, PyGotha...akaptur
Byterun is a Python interpreter written in Python with Ned Batchelder. It's architected to mirror the structure of CPython (and be more readable, too)! Learn how the interpreter is constructed, how ignorant the Python compiler is, and how you use a 1,500 line switch statement every day.
Pycon 2011 talk (may not be final, note)c.titus.brown
This document discusses handling large amounts of genomic data using probabilistic data structures like Bloom filters. Bloom filters allow storing and querying large amounts of genomic sequence data in a memory-efficient way. They can be used to assemble short DNA sequences, reduce graph complexity, and trim errors from assemblies. The approach works well for pre-filtering large metagenomic datasets, enabling assembly of 200GB datasets using a single machine.
Goptuna Distributed Bayesian Optimization Framework at Go Conference 2019 AutumnMasashi Shibata
1. The document describes using Goptuna, an open source Bayesian optimization library for Python and Go, to optimize hyperparameters for an ISUCON competition application.
2. It shows how Goptuna can suggest values for various configuration parameters like MySQL, Nginx, and Go application settings to optimize the application performance.
3. Running the optimization with Goptuna over 100 trials was able to find parameter configurations that improved the ISUCON score from 9560 to over 10,000 points.
The document discusses Python libraries and modules. It explains that libraries allow for organizing related functions hierarchically and avoiding duplication. Modules create namespaces and allow code reuse through importing. The key Python standard libraries are described, including math for mathematical functions and sys for system functions like getting command line arguments and the Python path. Import statements and conventions are covered for accessing library functionality.
This document provides a summary of a presentation on working with weather and climate data. It introduces Jupyter notebooks and APIs that can be used to retrieve weather forecast data and plot it. It demonstrates how to clean the data, convert it to a Pandas dataframe, and create visualizations of temperature and precipitation forecasts over time. Methods for interpolating point data onto grids and working with netCDF files are also briefly discussed.
The document discusses slicing in Python. It explains that lists, strings, and tuples can be sliced using a range of indices. It provides examples of slicing elements and strings, demonstrating how slicing returns a substring or subsequence. It notes that slicing always creates a new collection, rather than being an alias, so changes to a slice do not affect the original object.
PyCon 2011 talk - ngram assembly with Bloom filtersc.titus.brown
This document summarizes a talk about using probabilistic data structures like Bloom filters to handle large genomic and sequencing datasets. Bloom filters allow storing and querying enormous numbers of DNA fragments and sequences in a way that is memory efficient and scales to very large datasets. The talk describes how Bloom filters can be used to assemble genomes and reduce complexity in assembly graphs. While not perfect representations, Bloom filters enable genomic assembly and analysis that would otherwise not be possible given the volume of data.
This document summarizes key concepts about strings in Python. It explains that strings are sequences of characters that can be indexed and iterated over like lists. Strings are immutable and concatenated using +. Formatting uses % operator. Escape characters represent special characters. Triple quotes allow multi-line strings. Strings have useful methods like upper(), lower(), find(), replace(), etc. that can be chained together.
An enhanced version of the #codemesh2014 talk on network programming in Go. It covers HTTP, HTTPS, TCP/IP, TCP/IP over TLS, UDP and basic cryptographic functions with AES-CBC and RSA.
The document discusses advanced concepts in Python programming including scientific programming, graphics programming, network programming, GUI programming, and web programming. Scientific programming uses NumPy to work with multi-dimensional arrays and mathematical functions. Graphics programming draws shapes like circles using a graphics library. Network programming demonstrates a simple client-server program using sockets to send and receive data. GUI programming creates a button that displays a popup message when clicked. Web programming shows a simple form and CGI script to submit and display user input from the form.
This document contains a collection of Python tidbits and examples including:
- Examples of list comprehensions, generators, slicing, unpacking, and decorators
- Tracing a Fibonacci function using a decorator
- Examples demonstrating tuples, dictionaries, strings, iterators, and performance
- Brief mentions of additional Python topics like debugging, web frameworks, documentation tools, and libraries
This document discusses controlling an LED light using Bluetooth from a Raspberry Pi. It includes the Python code to set up a Bluetooth server on the Raspberry Pi, listen for connections, and turn the LED on or off depending on the data received over Bluetooth. It also mentions using App Inventor to create an app with blocks to control the LED over Bluetooth.
The document discusses aliasing in Python programming. It explains that an alias is a second name for a piece of data. It provides examples of how aliasing can cause bugs when working with mutable data like lists, but not with immutable data. It also discusses why Python allows aliasing despite the potential for bugs - for efficiency when working with large data structures and because sometimes in-place updates are desired.
Not Really Engineering, Barely a ScienceRod Begbie
Slide deck for a presentation I gave to teenagers at the National Student Leadership Conference in Berkley CA, to convince them that software engineering is the awesomest profession there is.
This tutorial discusses using Python, PuLP, and GLPK to solve linear programming problems. PuLP is a Python module that can generate LP files and interface with solvers like GLPK to solve linear problems. The tutorial covers using Python for programming, defining decision variables and constraints with PuLP, writing and solving LP models, and accessing solution results.
This document discusses Retrofit, an open source library that makes it easier to call REST APIs from Android and Java applications. It allows defining API endpoints as an interface and automatically serializes/deserializes the HTTP request and response. Retrofit handles network requests asynchronously and returns responses that can be easily converted to POJOs. It provides a simpler and more elegant way to make API calls compared to using raw HTTP libraries and manually parsing JSON responses.
Topics of Complex Social Networks: Domination, Influence and AssortativityMoses Boudourides
The document summarizes topics related to complex social networks, including domination, influence, and assortativity. It begins by defining dominating sets in graphs and their properties such as minimal dominating sets. It describes the complexity of computing dominating sets and provides algorithms. It then discusses egocentric subgraphs induced by dominating sets and the classification of vertices as private or public alters. Finally, it introduces notation used to describe edges between dominating sets, private alters, and public alters.
The document introduces Python programming language. It provides an overview of Python's history and key features such as being an interpreted, object-oriented, and platform independent language. It also discusses Python syntax including data types, variables, input/output, operators, conditional statements, loops, functions, and data structures like lists, tuples, dictionaries. Several examples are given to illustrate different Python concepts and syntax.
Diving into byte code optimization in python Chetan Giridhar
The document discusses byte-code optimization in Python. It begins by explaining that Python source code is compiled into byte code, which is then executed by the CPython interpreter. It describes some of the key steps in the compilation process, including parsing the source code and generating an abstract syntax tree (AST) before compiling to bytecodes. The document then discusses some approaches for optimizing Python code at the byte-code level, including using tools like Pyrex, Psyco and the Python bytecode manipulation library BytePlay. It recommends always profiling applications to identify optimization opportunities and considering writing performance-critical portions as C extensions.
The document provides an overview of the Go programming language, including its history, data types, basic syntax like variables and functions, and common constructs like arrays, slices, maps, and concurrency features. It was developed at Google in 2009 and aims to provide efficiency of static typing with ease of dynamic languages through features like garbage collection and good support for concurrency and communication.
Byterun, a Python bytecode interpreter - Allison Kaptur at NYCPythonakaptur
This document discusses Byterun, a Python interpreter written in Python. It explains how Python code is compiled to bytecode which is then interpreted. Key points made include:
- Python code is first compiled to bytecode, which is a sequence of bytes representing operations and arguments.
- The dis module can disassemble bytecode back into a human-readable format showing the instructions.
- The interpreter works by reading each bytecode instruction and carrying out the corresponding operation, such as loading variables or performing arithmetic.
- This dynamic execution allows Python to behave differently based on the types of values at runtime, like formatting strings.
Allison Kaptur: Bytes in the Machine: Inside the CPython interpreter, PyGotha...akaptur
Byterun is a Python interpreter written in Python with Ned Batchelder. It's architected to mirror the structure of CPython (and be more readable, too)! Learn how the interpreter is constructed, how ignorant the Python compiler is, and how you use a 1,500 line switch statement every day.
Pycon 2011 talk (may not be final, note)c.titus.brown
This document discusses handling large amounts of genomic data using probabilistic data structures like Bloom filters. Bloom filters allow storing and querying large amounts of genomic sequence data in a memory-efficient way. They can be used to assemble short DNA sequences, reduce graph complexity, and trim errors from assemblies. The approach works well for pre-filtering large metagenomic datasets, enabling assembly of 200GB datasets using a single machine.
Goptuna Distributed Bayesian Optimization Framework at Go Conference 2019 AutumnMasashi Shibata
1. The document describes using Goptuna, an open source Bayesian optimization library for Python and Go, to optimize hyperparameters for an ISUCON competition application.
2. It shows how Goptuna can suggest values for various configuration parameters like MySQL, Nginx, and Go application settings to optimize the application performance.
3. Running the optimization with Goptuna over 100 trials was able to find parameter configurations that improved the ISUCON score from 9560 to over 10,000 points.
The document discusses Python libraries and modules. It explains that libraries allow for organizing related functions hierarchically and avoiding duplication. Modules create namespaces and allow code reuse through importing. The key Python standard libraries are described, including math for mathematical functions and sys for system functions like getting command line arguments and the Python path. Import statements and conventions are covered for accessing library functionality.
This document provides a summary of a presentation on working with weather and climate data. It introduces Jupyter notebooks and APIs that can be used to retrieve weather forecast data and plot it. It demonstrates how to clean the data, convert it to a Pandas dataframe, and create visualizations of temperature and precipitation forecasts over time. Methods for interpolating point data onto grids and working with netCDF files are also briefly discussed.
The document discusses slicing in Python. It explains that lists, strings, and tuples can be sliced using a range of indices. It provides examples of slicing elements and strings, demonstrating how slicing returns a substring or subsequence. It notes that slicing always creates a new collection, rather than being an alias, so changes to a slice do not affect the original object.
PyCon 2011 talk - ngram assembly with Bloom filtersc.titus.brown
This document summarizes a talk about using probabilistic data structures like Bloom filters to handle large genomic and sequencing datasets. Bloom filters allow storing and querying enormous numbers of DNA fragments and sequences in a way that is memory efficient and scales to very large datasets. The talk describes how Bloom filters can be used to assemble genomes and reduce complexity in assembly graphs. While not perfect representations, Bloom filters enable genomic assembly and analysis that would otherwise not be possible given the volume of data.
This document summarizes key concepts about strings in Python. It explains that strings are sequences of characters that can be indexed and iterated over like lists. Strings are immutable and concatenated using +. Formatting uses % operator. Escape characters represent special characters. Triple quotes allow multi-line strings. Strings have useful methods like upper(), lower(), find(), replace(), etc. that can be chained together.
An enhanced version of the #codemesh2014 talk on network programming in Go. It covers HTTP, HTTPS, TCP/IP, TCP/IP over TLS, UDP and basic cryptographic functions with AES-CBC and RSA.
The document discusses advanced concepts in Python programming including scientific programming, graphics programming, network programming, GUI programming, and web programming. Scientific programming uses NumPy to work with multi-dimensional arrays and mathematical functions. Graphics programming draws shapes like circles using a graphics library. Network programming demonstrates a simple client-server program using sockets to send and receive data. GUI programming creates a button that displays a popup message when clicked. Web programming shows a simple form and CGI script to submit and display user input from the form.
This document contains a collection of Python tidbits and examples including:
- Examples of list comprehensions, generators, slicing, unpacking, and decorators
- Tracing a Fibonacci function using a decorator
- Examples demonstrating tuples, dictionaries, strings, iterators, and performance
- Brief mentions of additional Python topics like debugging, web frameworks, documentation tools, and libraries
This document discusses controlling an LED light using Bluetooth from a Raspberry Pi. It includes the Python code to set up a Bluetooth server on the Raspberry Pi, listen for connections, and turn the LED on or off depending on the data received over Bluetooth. It also mentions using App Inventor to create an app with blocks to control the LED over Bluetooth.
The document discusses aliasing in Python programming. It explains that an alias is a second name for a piece of data. It provides examples of how aliasing can cause bugs when working with mutable data like lists, but not with immutable data. It also discusses why Python allows aliasing despite the potential for bugs - for efficiency when working with large data structures and because sometimes in-place updates are desired.
Not Really Engineering, Barely a ScienceRod Begbie
Slide deck for a presentation I gave to teenagers at the National Student Leadership Conference in Berkley CA, to convince them that software engineering is the awesomest profession there is.
This tutorial discusses using Python, PuLP, and GLPK to solve linear programming problems. PuLP is a Python module that can generate LP files and interface with solvers like GLPK to solve linear problems. The tutorial covers using Python for programming, defining decision variables and constraints with PuLP, writing and solving LP models, and accessing solution results.
This document discusses Retrofit, an open source library that makes it easier to call REST APIs from Android and Java applications. It allows defining API endpoints as an interface and automatically serializes/deserializes the HTTP request and response. Retrofit handles network requests asynchronously and returns responses that can be easily converted to POJOs. It provides a simpler and more elegant way to make API calls compared to using raw HTTP libraries and manually parsing JSON responses.
Topics of Complex Social Networks: Domination, Influence and AssortativityMoses Boudourides
The document summarizes topics related to complex social networks, including domination, influence, and assortativity. It begins by defining dominating sets in graphs and their properties such as minimal dominating sets. It describes the complexity of computing dominating sets and provides algorithms. It then discusses egocentric subgraphs induced by dominating sets and the classification of vertices as private or public alters. Finally, it introduces notation used to describe edges between dominating sets, private alters, and public alters.
Boudourides: Risk in Social Networks: Network Influence & Selection on Minori...Moses Boudourides
This document summarizes a presentation on network influence and selection in minority games. It discusses how agents located on social networks make choices between two options in a minority game framework. The agents are influenced by their neighbors but can also change their network connections over time by selecting better performing neighbors and dropping lower performing links. Simulation results are presented showing how network structure and influence affect outcomes under different parameters. Future research directions are proposed, including extending the models to incorporate more agent types, spatial voting games, and testing models against real-world data.
Διακριτά Μαθηματικά ΙI: Εισαγωγή στη Συνδυαστική. Του Μωυσή ΜπουντουρίδηMoses Boudourides
Διακριτά Μαθηματικά ΙI: Εισαγωγή στη Συνδυαστική
Αυτό είναι το δεύτερο μέρος των σημειώσεων μου στο προπτυχιακό μάθημα των Διακριτών Μαθηματικών του Μαθηματικού Τμήματος του Πανεπιστημίου Πατρών (άνοιξη 2014). Το πρώτο τμήμα της εισαγωγής στην λογική είναι εδώ:
http://www.slideshare.net/MosesBoudourides/1-32613813
Digital, Humanities, Latour and Networks. By Moses A. BoudouridesMoses Boudourides
This document discusses using network analysis and digital tools to study literary texts. It describes representing elements of texts like characters or words as network nodes and their co-occurrences as connections. The document also discusses challenges in automatically extracting this type of network from texts and different ways time could be incorporated. Overall, the document explores how digital humanities and network analysis can provide new perspectives for understanding relationships in literary corpora.
My Great Grandfather, Thomas Frank Benson, was an engineer in the R.A.F. during WWII, he was stationed all around the UK and then in 1944 he was told to go to India. It was at this time he wrote a diary and took a vast amount of photos, showing every day life in India from the end of 1944 to the start of 1946. In a time where travelling around the world was extremely difficult and very uncommon, he got the chance to see and experience a country completely alien to 40s Britain and this book uncovers the experiences and emotions he had.
Hasil penelitian menunjukkan hubungan antara konsentrasi glukosa dengan persentase gum xantan yang dihasilkan. Percobaan menggunakan berbagai konsentrasi glukosa 4 gram, 6 gram, 8 gram, dan 10 gram. Hasilnya menunjukkan bahwa semakin besar konsentrasi glukosa, semakin besar pula persentase gum xantan yang dihasilkan, kecuali pada konsentrasi glukosa 10 gram.
Physical activity is shown to reduce the risk of cardiovascular disease. Early studies in the 1940s-1960s found lower rates of cardiovascular disease in bus conductors who climbed stairs daily compared to sedentary bus drivers. Later prospective studies established a dose-response relationship between physical activity levels and reduced risk of cardiovascular events. While more research is still needed, most studies find that both men and women can gain equivalent cardiovascular benefits from regular physical activity.
The document provides pricing and product details for furniture selections for a dining room, including three pendant lamps ranging in price from $399-$599, an area rug priced at $781, and three bar stool options priced between $438-$878 for pairs, with materials including wood, wicker, and leather.
This document provides information on products, services, training, and projects from Hypotech Solutions. It outlines security systems and industrial automation products. It describes placement, internship, inplant training, courses in embedded systems, VLSI, and MATLAB. It also details workshops on embedded systems and VLSI. Finally, it lists diploma, engineering, master's and PhD project opportunities.
NetworkX is a Python language software package and an open-source tool for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. NetworkX can load, store and analyze networks, generate new networks, build network models, and draw networks. It is a computational network modelling tool and not a software tool development. The first public release of the library, which is all based on Python, was in April 2005.
Towards neuralprocessingofgeneralpurposeapproximateprogramsParidha Saxena
Did validation of one of the machine learning algorithms of neural networks,and compared the results for its implementation on hardware (FPGA) using xilinx, with that of a sequential code execution(using FANN).
The document discusses using neural networks to accelerate general purpose programs through approximate computing. It describes generating training data from programs, using this data to train neural networks, and then running the neural networks at runtime instead of the original programs. Experimental results show the neural network implementations provided speedups of 10-900% compared to the original programs with minimal loss of accuracy. An FPGA implementation of the neural networks was also able to achieve further acceleration, running a network 4x faster than software.
Tensorflow in practice by Engineer - donghwi chaDonghwi Cha
- Tensorflow is an introduction to the machine learning framework Tensorflow covering key concepts like computation graphs, operations, sessions, training, replication, and clustering.
- Key aspects discussed include how Tensorflow executes operations as a static computation graph, uses sessions to run graphs and tensors to hold values, and supports data parallelism through replication across devices/workers.
- The document provides examples of building neural network models in Tensorflow and discusses techniques for training models like backpropagation and distributing training using data parallelism.
This document discusses machine learning on source code. It begins by defining machine learning on source code as applying machine learning where the input data is source code. It then discusses some of the challenges of applying machine learning to source code, including data retrieval and analysis. Finally, it provides examples of potential use cases like predicting the next token in code, learning to represent programs with graphs, and building tools to assist with code reviews.
This document discusses analyzing relationships and networks from data. It defines key network concepts like nodes, edges, and centrality measures. It also summarizes different ways of representing networks through matrices, lists, and graphs. Network analysis techniques are covered like centrality measures to identify important nodes, community detection to find groups, and measuring properties like path length and clustering. Visualization methods for networks like node-link diagrams and matrices are also mentioned. The document provides an overview of relationship and network analysis in data science.
The document discusses concurrency models and patterns in programming languages. It describes how features like first-class functions allow some patterns to be invisible in languages. Common patterns like threading and actors are discussed, along with implementations using Communicating Sequential Processes and the actor model in different languages. The goal is to irritate the reader by discussing these concepts.
A peek on numerical programming in perl and python e christopher dyken 2005Jules Krdenas
This document compares the numerical programming capabilities and performance of Perl and Python with and without numerical libraries like NumPy and PDL. It implements a trapezoidal quadrature rule to integrate three different functions in standard C, optimized C, Python, Python with NumPy, Python with numarray, Perl, and Perl with PDL. The results show that plain Python and Perl are much slower than C, but with numerical libraries their performance is comparable to optimized C for problems that can be formulated as element-by-element array operations. NumPy performs worse for simple functions but the gap decreases for more complex functions that use trigonometric operations. So for numerical problems, Python and Perl with add-on libraries can be viable alternatives to C/C
DSD-INT 2018 Work with iMOD MODFLOW models in Python - Visser BootsmaDeltares
Presentation by Martijn Visser and Huite Bootsma (Deltares) at the iMOD International User Day 2018, during Delft Software Days - Edition 2018. Tuesday 13 November 2018, Delft.
Python for Scientific Computing -- Ricardo Cruzrpmcruz
This document discusses Python for scientific computing. It provides notes on NumPy, the fundamental package for scientific computing in Python. NumPy allows vectorized mathematical operations on multidimensional arrays in a simple and efficient manner. The notes cover common NumPy operations and syntax as compared to MATLAB and R. Pandas is also introduced as a package for data manipulation and analysis based on the concept of data frames from R. Examples are given of generating fake data to demonstrate modeling capabilities in Python.
Exceeding Classical: Probabilistic Data Structures in Data Intensive Applicat...Andrii Gakhov
We interact with an increasing amount of data but classical data structures and algorithms can't fit our requirements anymore. This talk is to present the probabilistic algorithms and data structures and describe the main areas of their applications.
ScaleGraph - A High-Performance Library for Billion-Scale Graph AnalyticsToyotaro Suzumura
Please cite the following paper:
Toyotaro Suzumura and Koji. Ueno, "ScaleGraph: A high-performance library for billion-scale graph analytics," 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, 2015, pp. 76-84.
doi: 10.1109/BigData.2015.7363744
Recently, large-scale graph analytics has become a very popular topic owing to the emergence of gigantic graphs whose number of vertices and edges is in millions, billions or even trillions. Many graph analytics libraries and frameworks have been proposed with various computational models and programming languages to deal with such graphs. X10 programming language is a PGAS language that aims at both software performance and programmer's productivity. We introduce ScaleGraph library developed using X10 programming to illustrate the use of X10 for large-scale graph analytics. ScaleGraph library provides XPregel framework that is inspired by Google's Pregel computation model, serving as a building block for implementing graph kernels. We also optimized X10 runtime in some parts such as collective communication and memory management. We evaluated the performance and scalability of ScaleGraph libraries. The result shows that most graph kernels have good performance and scalability. ScaleGraph library is 9.4 times faster than Giraph in the experiment of PageRank with 16 machine nodes. To the best of our knowledge, ScaleGraph is the first X10-based library to address performance, scalability and productivity issues in dealing with large-scale graph analytics.
An introduction to Google's AI Engine, look deeper into Artificial Networks and Machine Learning. Appreciate how our simplest neural network be codified and be used to data analytics.
- The document discusses program synthesis through solving optimization problems to find the shortest program that fits the given observations and constraints.
- It proposes using probabilistic context-free grammars to define the search space of possible programs and casting the problem as finding a satisfying assignment for a set of constraints over the program variables.
- An iterative algorithm is described that finds program solutions, adds a minimum length constraint, and repeats to find shorter programs that still satisfy the constraints.
Python is a multi-paradigm programming language that can be used for scientific applications. It has libraries for tasks like data acquisition, analysis, and visualization. Examples shown include using Python to acquire data from instruments via VISA, analyze data with NumPy and SciPy, and create graphical user interfaces and visualizations with Matplotlib and PyQt. The document provides an overview of Python's capabilities and examples of code for common scientific computing tasks.
NSC #2 - D2 06 - Richard Johnson - SAGEly AdviceNoSuchCon
The document discusses automated testing techniques for software, including fuzzing and concolic testing. Fuzzing involves generating random inputs to exercise a program, while concolic testing uses symbolic execution to track data flows and observe how program logic is influenced by inputs. Concolic testing can generate inputs that cover more program states but requires instrumenting the code to analyze execution.
V design and implementation of network security using genetic algorithmeSAT Journals
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Ανάλυση Δικτύων με το NetworkX της Python: Μια προκαταρκτική (αλλά ημιτελής ως το καλοκαίρι 2014) εισαγωγή
1. Anˆlush DiktÔwn
me to NetworkX thc Python
Mwus c A. MpountourÐdhc
Tm ma Majhmatik¸n PanepisthmÐou Pˆtrac
mboudour@upatras.gr
22 NoembrÐou 2012
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
2. PÐnakac Perieqomènwn
1 Python kai NetworkX
2 Eisagwg Grˆfwn
Mh Kateujunìmenoi Grˆfoi
Kateujunìmenoi Grˆfoi
Grˆfoi me Bˆrh Akm¸n
DimereÐc Grˆfoi
Qarakthristikˆ (Attributes) Kìmbwn kai Akm¸n
TÔpoi Grˆfwn kai Sqesiak¸n Dedomènwn
Genn torec Prokataskeuasmènwn Grˆfwn
3 Diktuakˆ Mètra
BajmoÐ Kìmbwn
Kentrikìthtec Kìmbwn
Suntelest c Suss¸reushc kai Metabatikìthta
Amoibaiìthta
Apostˆseic
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
3. 4 DiktuakoÐ DiamerismoÐ
Sunist¸sec kai KlÐkec
k–Pur nec (k–Cores)
Blockmodeling
Koinìthtec (Communities)
5 Diktuakˆ Montèla
Anameiximìthta (Assortativity)
Epirro kai Diˆqush
DÐktua Mikr¸n Kìsmwn (Small–Worlds)
DÐktua QwrÐc KlÐmaka (Scale–Free)
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
4. Python kai NetworkX
H Python eÐnai mia ermhneutik gl¸ssa, dhlad ,
ekteleÐtai qwrÐc na qreiˆzetai na perˆsei apì thn
diadikasÐa thc sÔntaxhc.
H Python diatÐjetai gia katèbasma (mazÐ me odhgÐec
egkatˆstashc) gia ìla ta leitourgikˆ sust mata
apì to http://www.python.org/download/. IdiaÐtera
gia ta Windows sunistˆtai h ActivePython.
To NetworkX eÐnai èna pakèto thc Python gia thn
dhmiourgÐa kai ton qeirismì grˆfwn kai diktÔwn.
To pakèto, h tekmhrÐwsh ki ˆlloi pìroi tou
NetworkX eÐnai diajèsimoi sto
http://networkx.lanl.gov/, ìpwc kai sthn apoj kh
pakètwn thc Python PyPI.
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
5. Mia apeujeÐac (one–click) egkatˆstash thc Python,
tou NetworkX ki ˆllwn monˆdwn thc Python mporeÐ
na gÐnei apì thn dianom thc Python Enthought,
pou dÐnetai dwreˆn gia akadhmaðk qr sh.
Pollèc qr simec plhroforÐec gia thn egkatˆstash
twn Python, NetworkX ki ˆllwn aparaÐthtwn
upologistik¸n pìrwn dÐnontai sth selÐda twn
Resources tou maj matoc Comp 200 tou
PanepisthmÐou tou Rice.
'Oloi oi k¸dikec twn diafanei¸n ed¸ èqoun
dokimasjeÐ pˆnw sthn èkdosh 2.7.3 thc Python kai
thn èkdosh 1.7 tou NetworkX.
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
6. Sto prooÐmio thc graf c (script) ìlwn twn
ekteloÔmenwn ed¸ kwdÐkwn thc Python ja prèpei
na eisˆgontai oi ex c grammèc:
import networkx as nx
import matplotlib . pyplot as plt
import pylab
Sthn antigraf ki epikìllhsh twn entol¸n pou
dÐnontai sth sunèqeia prèpei na proseqjeÐ h
stoÐqish stic allagèc gramm¸n, giatÐ h Python
eÐnai euaÐsjhth wc proc thn autìmath topojèthsh
esoq¸n (indentation).
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
7. Eisagwg Grˆfwn
Pr¸ta, dÐnontai oi entolèc gia mh kateujunìmenouc
grˆfouc.
Arqikˆ, dhmiourgeÐtai ènac kenìc grˆfoc:
G = nx. Graph ()
Metˆ, eisˆgontai arqikˆ oi kìmboi, p.q., oi 5
kìmboi '1', '2', '3', '4' kai '5' mpaÐnoun wc ex c:
G. add_nodes_from ([1,2,3,4,5])
Bèbaia, antÐ gia arijmoÔc, oi kìmboi mporoÔn na
eisˆgontai wc onìmata lèxeic, p.q.:
G. add_nodes_from ([ 'John ', 'Mary '])
G. add_node (" London ")
G. add_nodes_from ([ 'a','b','c','d','e'])
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
8. Tèloc, eisˆgontai oi akmèc, p.q., gia touc 5
kìmbouc 1–5 na 6 sundèseic:
G. add_edges_from ([(1,2),(1,4),(2,3),(3,4),(3,5),(4,5 )])
Oi lÐstec kai to pl joc twn eisaqjèntwn kìmbwn
kai akm¸n dÐnontai apì tic entolèc:
G. nodes ()
G. number_of_nodes ()
G. edges ()
G. number_of_edges ()
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
9. Gia to sqediasmì tou eisaqjèntoc grˆfou, p.q,
tou paradeÐgmatoc autoÔ tou grˆfou me 5
kìmbouc kai 6 akmèc, ekteloÔntai oi entolèc:
plt . figure ()
nx. draw (G)
plt . show ( block = False )
H teleutaÐa entol dèqetai diˆforec paramètrouc,
ìpwc, p.q., gia diaforetikì tÔpo sqedÐou (layout),
mègejoc kai qr¸ma kìmbwn kai afaÐresh twn
onomˆtwn (ids) twn kìmbwn:
nx. draw_spring (G, node_size =100 , node_color ='# A0CBE2 ',
with_labels = False )
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
10. ParaleÐpontac parametropoi seic, o sqediasmìc
tou grˆfou tou paradeÐgmatoc dÐnei to sq ma:
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
11. Oi kìmboi mporoÔn na topotethjoÔn se stajerèc jèseic,
efìson o grˆfoc eÐqe eisaqjeÐ me tic suntetagmènec
touc, pou mporeÐ na gÐnei, p.q., sto parˆdeigma autì
wc ex c:
pos ={1:(0,0),2:(1,0),4:(0,1),3:(1,1),5:(0.5,2.0)}
nx. draw (G, pos )
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
12. Eisagwg Kateujunìmenwn Grˆfwn
GÐnetai wc ex c, p.q., sto parˆdeigma autì:
G=nx. DiGraph ()
G. add_nodes_from (["A","B","C","D"])
G. add_edges_from ([( "A","B"), ("C","A"), ("C","B"),
("B","D")])
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
13. Eisagwg Grˆfwn me Bˆrh Akm¸n
GÐnetai, p.q., sto parˆdeigma autì me tic entolèc:
G=nx. Graph ()
G. add_weighted_edges_from ([( 'a','b',4),( 'a','c',8),
('a','d',5),( 'c','d',3)])
kai o sqediasmìc wc ex c:
pos =nx. spring_layout (G)
edge_labels = dict ([((u,v ,),d['weight '])
for u,v,d in G. edges ( data = True )])
nx. draw_networkx_nodes (G,pos , node_size = 700)
nx. draw_networkx_edges (G, pos)
nx. draw_networkx_labels (G,pos , font_size =20)
nx. draw_networkx_edge_labels (G,pos ,
edge_labels = edge_labels , font_size =20)
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
15. AntÐ thc parˆjeshc twn tim¸n twn bar¸n, oi akmèc mporoÔn na
sqediasjoÔn me eÔrh anˆloga twn tim¸n twn bar¸n wc ex c:
nx. draw_networkx_nodes (G,pos , node_size = 700)
edgewidth =[]
for (u,v,d) in G. edges ( data = True ):
edgewidth . append (d['weight '])
nx. draw_networkx_edges (G,pos , edge_color ='b',
width = edgewidth )
nx. draw_networkx_labels (G,pos , font_size =20)
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
16. Epiplèon, mporoÔn na krathjoÔn sto sqèdio mìno oi akmèc me bˆrh
megalÔtera kˆpoiac endiˆmeshc tim c, p.q., 4:
elarge = [(u,v) for (u,v,d) in G. edges ( data = True )
if d['weight '] >4]
esmall = [(u,v) for (u,v,d) in G. edges ( data = True )
if d['weight '] <=4]
nx. draw_networkx_edges (G,pos , edgelist = elarge ,
edge_color ='b',width = edgewidth )
nx. draw_networkx_edges (G,pos , edgelist = esmall , width =6,
alpha =0.5, edge_color ='g',style ='dashed ')
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
17. Eisagwg Dimer¸n Grˆfwn
H eisagwg dimer¸n grˆfwn gÐnetai ìpwc, p.q.,
sto parakˆtw parˆdeigma:
from networkx . algorithms import bipartite
G = nx. Graph ()
G. add_nodes_from ([1,2,3,4], bipartite =0)
G. add_nodes_from ([ 'a','b','c'], bipartite =1)
G. add_edges_from ([(1,'a'),(1,'b'),(2,'a'),(2,'b'),
(2,'c'),(3,'c'),(4,'b'),(4,'c')])
pos ={1:(0,0),
2:(0,1),
3:(0,2),
4:(0,3),
'a':(1,0.5),
'b':(1,1.5),
'c':(1,2.5)}
O èlegqoc an prìkeitai perÐ dimeroÔc grˆfou
(True) ìqi (False) gÐnetai me thn entol :
print bipartite . is_bipartite (G)
True
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
18. O sqediasmìc tou dimeroÔc grˆfou gÐnetai wc
ex c:
mode1 , mode2 = bipartite . sets (G)
nx. draw_networkx_nodes (G,pos , nodelist = list ( mode1 ),
node_color ='b',node_size = 700)
nx. draw_networkx_nodes (G,pos , nodelist = list ( mode2 ),
node_color ='g',node_size = 700)
nx. draw_networkx_edges (G, pos)
nx. draw_networkx_labels (G,pos , font_size =20 ,
font_color ='# FFFFFF ')
plt . axis ('off ')
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
23. BajmoÐ Kìmbwn
Pr¸ta, gia mh kateujunìmenouc grˆfouc: O grˆfoc
G = (V, E) sto sÔnolo koruf¸n V eÐnai mh kateujunìmenoc, ìtan to
sÔnolo twn akm¸n tou E eÐnai èna summetrikì uposÔnolo tou V V.
GeÐtonec kìmbwn: Gia duo korufèc i, j 2 V tou G, h j lègetai
geÐtonac thc i ìtan (i, j) 2 E.
G. nodes () # List of nodes
G. number_of_nodes () # Number of nodes
len (G) # Number of nodes
G. order () # Number of nodes
G. number_of_edges () # Number of of edges
G. neighbors (1) # Neighbors of node 1
## To get the list of neighbors of all nodes :
for node in G. nodes ():
print node , G. neighbors ( node )
PÐnakac GeitnÐashc (Adjacency Matrix): EÐnai ènac
(summetrikìc) pÐnakac A = fAgi,j2V tˆxhc jVj jVj tètoioc ¸ste
A = 1, ìtan i, j geÐtonec, A = 0, diaforetikˆ.
A = nx. to_numpy_matrix (G)
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
24. BajmoÐ kìmbwn: Sto mh kateujunìmeno grˆfo G, o
bajmìc miac koruf c i, pou sumbolÐzetai wc ki,
orÐzetai san to pl joc twn geitìnwn tou i,
dhlad , to pl joc twn sundèsewn pou
prospÐptoun sto i. Profan¸c, isqÔei:
ki =
X
j2V
A =
X
i2V
A
ki, epiplèon,
X
i2V
ki =
X
i,j2V
A = 2jEj
G. degree (1) # Degree of node 1
len (G. neighbors (1) # Degree of node 1
G. degree () # List of degrees of all nodes
## To get the table of degrees of all nodes :
for node in G. nodes ():
print node , G. degree ( node )
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
25. BajmoÐ Kìmbwn Kateujunìmenwn Grˆfwn
'Estw o kateujunìmenoc grˆfoc G = (V, E) sto sÔnolo koruf¸n V,
ìpou t¸ra to sÔnolo twn akm¸n tou E eÐnai èna mh summetrikì
uposÔnolo tou V V ki o antÐstoiqoc pÐnakac geitnÐashc A = fAg
eÐnai mh summetrikìc.
O bajmìc eisìdou thc koruf c i tou G, pou sumbolÐzetai wc kin i ,
orÐzetai san to pl joc twn sundèsewn pou xekinoÔn apì geÐtonec
tou i kai kateujÔnontai proc ton i, dhlad ,
kin
i =
X
j2V
A
O bajmìc exìdou thc koruf c i tou G, pou sumbolÐzetai wc kout i ,
orÐzetai san to pl joc twn sundèsewn pou xekinoÔn apì ton i kai
kateujÔnontai proc geÐtonec tou i, dhlad ,
kout
i =
X
i2V
A
Profan¸c, isqÔei:
X
i2V
kin
i =
X
j2V
kout
i =
X
i,j2V
A = jEj
G. in_degree (1) #In - Degree of node 1
G. out_degree (1) #Out - Degree of node 1
G. in_degree () # List of in - degrees of all nodes
G. out_degree () # List of out - degrees of all nodes
## To get the table of in - degrees and out - degrees of all nodes :
for node in G. nodes ():
print node , G. in_degree ( node ), G. out_degree ( node )
Mwus c A. MpountourÐdhc Anˆlush DiktÔwn me to NetworkX thc Python
26. Katanomèc Bajm¸n
Istìgramma katanom c bajm¸n (gia mh kateujunìmeno grˆfo):
degrees = G. degree ()
values = sorted ( set ( degrees . values ()))
hist = [ degrees . values (). count (x) for x in values ]
fig = plt . figure ()
ax = fig . add_subplot ( 111 )
plt . plot ( values ,hist ,'ro -')
#ax . set_xscale (' log ') # Logarithmic x scale
#ax . set_yscale (' log ') # Logarithmic y scale
plt . title ( Degree Distribution )
plt . xlabel ('Degree ')
plt . ylabel ('Number of nodes ')
Istìgramma katanom c bajm¸n (gia kateujunìmeno grˆfo):
## in_degrees = G. in_degree ()
in_values = sorted ( set ( in_degrees . values ()))
in_hist = [ in_degrees . values (). count (x) for x in in_values ]
out_degrees = G. out_degree ()
out_values = sorted ( set ( out_degrees . values ()))
out_hist = [ out_degrees . values (). count (x) for x in out_values ]
fig = plt . figure ()
ax = fig . add_subplot ( 111 )
plt . plot ( in_values , in_hist ,'ro -')
plt . plot ( out_values , out_hist ,'bv -')
#ax . set_xscale (' log ') # Logarithmic x scale
#ax . set_yscale (' log ') # Logarithmic y scale
plt . titke (In - Degree Out - Degree Distributions )
plt . xlabel ('Degree ')
plt . ylabel ('Number of nodes ')
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27. Parˆdeigma: To dÐktuo karˆte
G = nx. karate_club_graph ()
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29. Kentrikìthtec Kìmbwn:
1. Kentrikìthta BajmoÔ (Degree Centrality)
Oi orismoÐ OLWN twn kentrikìthtwn pou ja d¸soume
ed¸ kai sth sunèqeia aforoÔn mh kateujunìmenouc
(aploÔc) grˆfouc.
H kentrikìthta bajmoÔ (degree centrality) xi tou kìmbou
i isoÔtai proc ton bajmì ki tou kìmbou autoÔ:
xi = ki
x8 = 5
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30. 2. Kentrikìthta Endiamesìthtac
(Betweenness Centrality)
H kentrikìthta endiamesìthtac (betweenness centrality) xi tou kìmbou
i isoÔtai proc:
xi =
X
s6=i6=t2V
nist
gst
ìpou nist eÐnai to pl joc twn gewdaitik¸n diadrom¸n metaxÔ twn
kìmbwn s kai t, pou pernoÔn apì ton kìmbo i, kai gst eÐnai to sunolikì
pl joc twn gewdaitik¸n diadrom¸n metaxÔ twn kìmbwn s kai t.
n23
,23 = 2
g3,23 = 4
x2 = 0.1436
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31. 3. Kentrikìthta EggÔthtac
(Closeness Centrality)
H kentrikìthta eggÔthtac (closeness centrality) xi tou kìmbou i
isoÔtai proc:
xi =
n P
j2V d
ìpou n eÐnai to pl joc twn kìmbwn tou grˆfou kai d h gewdaitik
apìstash apì ton kìmbo i proc opoiod pote ˆllo kìmbo j.
x0 = 0.5689
x2 = 0.5593
x33 = 0.55
x31 = 0.5409
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32. 4. Kentrikìthta IdiodianÔsmatoc
(Eigenvector Centrality)
H kentrikìthta idiodianÔsmatoc (eigenvector centrality) xi tou kìmbou i
isoÔtai proc:
xi = 1
1
X
j2V
Axj
ìpou A eÐnai o pÐnakac geitnÐashc (adjacency matrix) tou grˆfou kai
xi eÐnai oi sunist¸sec tou idiadianÔsmatoc tou A, pou antistoiqoÔn
sth megalÔterh idiotim tou 1.
x33 = 0.3734
x0 = 0.3555
x2 = 0.3172
x32 = 0.3086
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35. Kentrikìthta Mikr tim Megˆlh tim
Degree LÐgoi geÐtonec (sundèseic) PolloÐ geÐtonec (sundèseic)
Betweenness Mikrìc èlegqoc ro c Megˆloc èlegqoc ro c
Closeness Proc thn perifèreia Proc to kèntro
Eigenvector LÐgoi lÐgo shmantikoÐ geÐtonec PolloÐ polÔ shmantikoÐ geÐtonec
To dÐktuo twn stratiwtik¸n tou David Krackhardt:
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36. Suntelest c Suss¸reushc
O suntelest c suss¸reushc (clustering coefficient) Ci tou kìmbou i
orÐzetai wc:
Ci =
2i
ki(ki 1)
,
ìpou i eÐnai to pl joc twn sundèsewn metaxÔ twn geitonik¸n
kìmbwn tou i kai d i proc opoiod pote ˆllo kìmbo ki eÐnai to
pl joc twn geitonik¸n kìmbwn tou i.
nx. clustering (G) # Clustering coefficients of all nodes
nx. clustering (G,n) # Clustering coefficient of node n
C23 =
2 4
5 4 = 0.4
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37. Diktuak Metabatikìthta
O sunolikìc suntelest c suss¸reushc (global clustering coefficient)
(ìlou) tou grˆfou G orÐzetai wc h mèsh tim twn suntelest¸n
suss¸reushc twn kìmbwn tou:
C(G) =
1
jVj
X
i
Ci
H metabatikìthta (transitivity) tou grˆfou G orÐzetai wc to phlÐko:
T(G) =
pl joc trig¸nwn
pl joc sundedemènwn triˆdwn
nx. average_clustering (G)
# Graph clustering coefficient
nx. transitivity (G)
# Graph transitivity
C(G) = 0.16
T(G) = 0.19
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38. Amoibaiìthta Sundèsewn se Kateujunìmeno Grˆfo
Se ènan kateujunìmeno grˆfo, o suntelest c amoibaiìthtac
sundèsewn/desm¸n (link/tie mutuality coefficient) orÐzetai wc ex c:
M(G) =
pl joc antapodidìmenwn sundèsewn
pl joc ìlwn twn sundèsewn/tìxwn
G. to_undirected ( True ). size ()
# Reciprocated edges
G. number_of_edges ()
# Total edges
Er(G) = 64
E(G) = 195
M(G) = 0.3282
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39. Apostˆseic
Se ènan grˆfo G, gia kˆje duo kìmbouc i, j, h (gewdaitik ) apìstas
touc d(i, j) orÐzetai wc to m koc thc suntomìterhc diadrom c apì to i
sto j, efìson oi kìmboi autoÐ eÐnai sundedemènoi, en¸ d(i, j) = 1,
diaforetikˆ (kai fusikˆ, d(i, i) = 0). (H ‘‘suntomìterh diadrom ’’
metaxÔ duo kìmbwn eÐnai h diadrom pou èqei to elˆqisto m koc
anˆmesa se ìlec tic diadromèc metaxÔ twn duo kìmbwn.)
To mèso m koc suntomìterhc diadrom c (average shortest path length)
orÐzetai wc ex c:
a =
1
jVj(jVj 1)
X
i,j2V
d(i, j)
a = nx. average_shortest_path_length (G)
a = 2.4082
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40. Mètra Diˆforwn Empeirik¸n DiktÔwn TABLE I. The general characteristics of several real networks. For each network we indicated the number of nodes, the average
degree !k, the average path length ! and the clustering coefficient C. For a comparison we have included the average path
length !rand and clustering coefficient Crand of a random graph with the same size and average degree. The last column
identifies the symbols in Figs. 8 and 9.
Network Size !k ! !rand C Crand Reference Nr.
WWW, site level, undir. 153, 127 35.21 3.1 3.35 0.1078 0.00023 Adamic 1999 Internet, domain level 3015 - 6209 3.52 - 4.11 3.7 - 3.76 6.36 - 6.18 0.18 - 0.3 0.001 Yook et al. 2001a,
Pastor-Satorras et al. 2001 Movie actors 225, 226 61 3.65 2.99 0.79 0.00027 Watts, Strogatz 1998 LANL coauthorship 52, 909 9.7 5.9 4.79 0.43 1.8 × 10−4 Newman 2001a,b MEDLINE coauthorship 1, 520, 251 18.1 4.6 4.91 0.066 1.1 × 10−5 Newman 2001a,b SPIRES coauthorship 56, 627 173 4.0 2.12 0.726 0.003 Newman 2001a,b,c NCSTRL coauthorship 11, 994 3.59 9.7 7.34 0.496 3 × 10−4 Newman 2001a,b Math coauthorship 70, 975 3.9 9.5 8.2 0.59 5.4 × 10−5 Barab´asi et al. 2001 Neurosci. coauthorship 209, 293 11.5 6 5.01 0.76 5.5 × 10−5 Barab´asi et al. 2001 E. coli, substrate graph 282 7.35 2.9 3.04 0.32 0.026 Wagner, Fell 2000 10
E. coli, reaction graph 315 28.3 2.62 1.98 0.59 0.09 Wagner, Fell 2000 11
Ythan estuary food web 134 8.7 2.43 2.26 0.22 0.06 Montoya, Sol´e 2000 12
Silwood park food web 154 4.75 3.40 3.23 0.15 0.03 Montoya, Sol´e 2000 13
Words, cooccurence 460.902 70.13 2.67 3.03 0.437 0.0001 Cancho, Sol´e 2001 14
Words, synonyms 22, 311 13.48 4.5 3.84 0.7 0.0006 Yook et al. 2001 15
Power grid 4, 941 2.67 18.7 12.4 0.08 0.005 Watts, Strogatz 1998 16
C. Elegans 282 14 2.65 2.25 0.28 0.05 Watts, Strogatz 1998 17
TABLE II. The scaling exponents characterizing the degree distribution of several scale-free networks, for which P(k) follows
a power-law (2). We indicate the size of the network, its average degree !k and the cutoff for the power-law scaling. For
directed networks we list separately the indegree (#in) and outdegree (#out) exponents, while for the undirected networks,
marked with a star, these values are identical. The columns lreal , lrand and lpow compare the average path length of real
networks with power-law degree distribution and the prediction of random graph theory (17) and that of Newman, Strogatz
and Watts (2000) (62), as discussed in Sect. V. The last column identifies the symbols in Figs. 8 and 9.
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41. Sunist¸sec kai KlÐkec
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42. Sunist¸sec kai KlÐkec
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