Evolving multi level graph

•Download as PPTX, PDF•

0 likes•15 views

Shahid Beheshti University

Evolving Multi-level Graph Partitioning Algorithms

Engineering

Introduction
Graph Partitioning
Evolutionary
Computation
Genetic
Programming
Review
Primitive
Operation Set
Experiment
Results
Conclusion
Future Work

 organizing parallel computation
workload
VLSI layout design
image processing
critical infrastructure protection
optimal graph partitioning is known to
be NP-hard
Introduction

 The total weight for a set of vertices X is given by:
the total weight of a set of edges Y is:
A balanced partition requires that:
Graph Partitioning

Coarsening:
 Random matching
 Light edge matching
 Heavy edge matching
Partition
 Graph growing partition (GGP)
 Greedy graph growing partition (GGGP)
Multi-level Graph Partitioning

Uncoarsening and Refinement:
 KL refinement
 Greedy refinement
Multi-level Graph Partitioning

Initialization
Parent selection
Recombination
Mutation
Survival selection
Termination
Evolutionary Computation

Erdos-R ¨ enyi
Barab ´ asi- ´ Albert
Los Alamos National Laboratory (LANL)
One month of the network data set was modeled
as a bipartite graph with 9,924 user vertices,
14,822 computer vertices, and 106,693
authentication edges. Subgraphs were created by
inducing the set of vertices visited by a random
walk of the total graph.
Experiment

Although there are a variety of multi-level
partitioning algorithms available, most are
intended to serve as general purpose solutions.
An obvious continuation of this work would be to
include the consideration of execution time.
Instead of focusing on reducing the cost of the
partitions produced, evolved partitioners could
instead aim to reduce the time needed to partition
certain types of graphs for time-sensitive
applications.
Conclusion & Future Work

Similar to Evolving multi level graph

Efficient aggregation for graph summarization

aftab alam

Graph Signal Processing (GSP) is a promising framework to analyze multi-dimensional neuroimaging datasets, while taking into account both the spatial and functional dependencies between brain signals. In the present work, we apply dimensionality reduction techniques based on graph representations of the brain to decode brain activity from real and simulated fMRI datasets. We introduce seven graphs obtained from a) geometric structure and/or b) functional connectivity between brain areas at rest, and compare them when performing dimension reduction for classification. We show that mixed graphs using both a) and b) offer the best performance. We also show that graph sampling methods perform better than classical dimension reduction including Principal Component Analysis (PCA) and Independent Component Analysis (ICA).

Evaluating Graph Signal Processing for Neuroimaging Through Classification an...

Nicolas Farrugia

Poster given at the Bioinformatics Open Source Conference (BOSC) and Intelligent Systems for Molecular Biology (ISMB) Conference held July 8-12, 2016 in Orlando, Florida, entitled GRNmap and GRNsight: open source software for dynamical systems modeling and visualization of medium-scale gene regulatory networks. Abstract: A gene regulatory network (GRN) consists of genes, transcription factors, and the regulatory connections between them that govern the level of expression of mRNA and proteins from those genes. Our open source MATLAB software package, GRNmap (http://kdahlquist.github.io/GRNmap/), uses ordinary differential equations to model the dynamics of medium-scale GRNs. The program uses a penalized least squares approach (Dahlquist et al. 2015, DOI: 10.1007/s11538-015-0092-6) to estimate production rates, expression thresholds, and regulatory weights for each transcription factor in the network based on gene expression data, and then performs a forward simulation of the dynamics of the network. GRNmap has options for using a sigmoidal or Michaelis-Menten production function. Parameters for a series of related networks, ranging in size from 15 to 35 genes, were optimized against DNA microarray data measuring the transcriptional response to cold shock in budding yeast, Saccharomyces cerevisiae, for the wild type strain and strains deleted for the transcription factors Cin5, Gln3, Hap4, Hmo1, and Zap1, giving biological insights into this process. GRNsight is an open source web application for visualizing such models of gene regulatory networks (http://dondi.github.io/GRNsight/index.html). GRNsight accepts GRNmap- or user-generated Excel spreadsheets containing an adjacency matrix representation of the GRN and automatically lays out the graph. The application colors the edges and adjusts their thicknesses based on the sign (activation or repression) and the strength (magnitude) of the regulatory relationship, respectively. Users can then modify the graph to define the best visual layout for the network. This work was partially supported by NSF award 0921038.

Dahlquist et-al bosc-ismb_2016_poster

GRNsight

Parallel Biological Sequence Comparison in GPU Platforms

Ganesan Narayanasamy

Graph neural networks (GNNs) are a new topic of research in data science where data structure graphs are used as important components for developing and training neural networks. GNN always learns the weight importance of the neighbor for perform message aggregation in which the feature vectors of all neighbors are aggregated without considering whether the features are useful or not. Using such more informative features positively affect the performance of the GNN model. So, in this paper i) after selecting a subset of features to define important node features, we present new graph features’ explanation methods based on graph centrality measures to capture rich information and determine the most important node in a network. Through our experiments, we find that selecting certain subsets of these features and adding other features based on centrality measure can lead to better performance across a variety of datasets and ii) we introduce a major design strategy for graph neural networks. Specifically, we suggest using batch renormalization as normalization over GNN layers. Combining these techniques, representing features based on centrality measures that passed to multilayer perceptron (MLP) layer which is then passed to adjusted GNN layer, the proposed model achieves greater accuracy than modern GNN models.

Node classification with graph neural network based centrality measures and f...

IJECEIAES

By Dmitry Storcheus (Engineer, Google Research) Feature extraction, as usually understood, seeks an optimal transformation from raw data into features that can be used as an input for a learning algorithm. In recent times this problem has been attacked using a growing number of diverse techniques that originated in separate research communities: from PCA and LDA to manifold and metric learning. The goal of this talk is to contrast and compare feature extraction techniques coming from different machine learning areas as well as discuss the modern challenges and open problems in feature extraction. Moreover, this talk will suggest novel solutions to some of the challenges discussed, particularly to coupled feature extraction.

DataEngConf: Feature Extraction: Modern Questions and Challenges at Google

Hakka Labs

NS-CUK Seminar: S.T.Nguyen, Review on "Improving Graph Neural Network Express...

ssuser4b1f48

Deep Learning in Computer Vision

Sungjoon Choi

HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...

AMD Developer Central

A quantum-inspired optimization heuristic for the multiple sequence alignment...

Konstantinos Giannakis

Artificial Intelligence Applications in Petroleum Engineering - Part I

Ramez Abdalla, M.Sc

Cz24655657

IJERA Editor

This research aims to develop kernel GNG, a kernelized version of the growing neural gas (GNG) algorithm, and to investigate the features of the networks generated by the kernel GNG. The GNG is an unsupervised artificial neural network that can transform a dataset into an undirected graph, thereby extracting the features of the dataset as a graph. The GNG is widely used in vector quantization, clustering, and 3D graphics. Kernel methods are often used to map a dataset to feature space, with support vector machines being the most prominent application. This paper introduces the kernel GNG approach and explores the characteristics of the networks generated by kernel GNG. Five kernels, including Gaussian, Laplacian, Cauchy, inverse multiquadric, and log kernels, are used in this study. The results of this study show that the average degree and the average clustering coefficient decrease as the kernel parameter increases for Gaussian, Laplacian, Cauchy, and IMQ kernels. If we avoid more edges and a higher clustering coefficient (or more triangles), the kernel GNG with a larger value of the parameter will be more appropriate.

Characteristics of Networks Generated by Kernel Growing Neural Gas

gerogepatton

CHARACTERISTICS OF NETWORKS GENERATED BY KERNEL GROWING NEURAL GAS

ijaia

Mobile Ad hoc Network (MANET) is a kind of self configuring and self describing wireless ad hoc networks. MANET has characteristics of topology dynamics due to factors such as energy conservation and node movement that leads to dynamic load balanced clustering problem (DLBCP). It is necessary to have an effective clustering algorithm for adapting the topology change. Generally, Clustering is mainly used to reduce the topology size. In this, we used load balance and energy metric in GA to solve the DLBCP. It is important to select the energy efficient cluster head for maintaining the cluster structure and balance the load effectively. Elitism based Immigrants Genetic algorithm (EIGA) and Memory Enhanced Genetic Algorithm (MEGA) are used to solve DLBCP. These schemes will select the optimal cluster head by considering the parameters includes distance and energy. We used EIGA to maintain the diversity level of the population and memory scheme (MEGA) to store the old environments into the memory. It promises the energy efficiency for the entire cluster structure to increase the lifetime of the network. The experimental results show that the proposed schemes increases the network life time and reduces the energy consumption.

Enhancing energy efficient dynamic load balanced clustering protocol using Dy...

IJTET Journal

P0126557 slides

Nguyen Chien

All projects

Karishma Jain

A comparison-of-first-and-second-order-training-algorithms-for-artificial-neu...

Cemal Ardil

Genetic Programming-based Evolutionary Feature Construction for Heterogeneous...

Hengzhe Zhang

ResNet, short for "Residual Network," is a type of deep neural network architecture that was introduced by Microsoft researchers in 2015. ResNet is designed to address the problem of vanishing gradients, which can occur in deep neural networks that are many layers deep. The main innovation in ResNet is the use of residual connections, also known as skip connections. These connections allow information from earlier layers of the network to bypass some of the later layers and be directly fed into the later layers. This helps to ensure that the gradient signal from the output can propagate back through the network during training, which can help to prevent the vanishing gradient problem. ResNet has been shown to be very effective at image recognition and other computer vision tasks. It has achieved state-of-the-art performance on a number of benchmark datasets, such as ImageNet. Since its introduction, many variations and improvements to the original ResNet architecture have been proposed, including ResNeXt, Wide ResNet, and Residual Attention Network (RANet).

Resnet.pdf

YanhuaSi

Similar to Evolving multi level graph (20)

Efficient aggregation for graph summarization

Evaluating Graph Signal Processing for Neuroimaging Through Classification an...

Dahlquist et-al bosc-ismb_2016_poster

Parallel Biological Sequence Comparison in GPU Platforms

Node classification with graph neural network based centrality measures and f...

DataEngConf: Feature Extraction: Modern Questions and Challenges at Google

NS-CUK Seminar: S.T.Nguyen, Review on "Improving Graph Neural Network Express...

Deep Learning in Computer Vision

HC-4012, Complex Network Clustering Using GPU-based Parallel Non-negative Mat...

A quantum-inspired optimization heuristic for the multiple sequence alignment...

Artificial Intelligence Applications in Petroleum Engineering - Part I

Cz24655657

Characteristics of Networks Generated by Kernel Growing Neural Gas

CHARACTERISTICS OF NETWORKS GENERATED BY KERNEL GROWING NEURAL GAS

Enhancing energy efficient dynamic load balanced clustering protocol using Dy...

P0126557 slides

All projects

A comparison-of-first-and-second-order-training-algorithms-for-artificial-neu...

Genetic Programming-based Evolutionary Feature Construction for Heterogeneous...

Resnet.pdf

Recently uploaded

The project Hospital Management system includes registration of patients, storing their details into the system, and also computerized billing in the pharmacy, and labs. The software has the facility to give a unique id for every patient and stores the details of every patient and the staff automatically. It includes a search facility to know the current status of each room. User can search availability of a doctor and the details of a patient using the id. The Hospital Management System can be entered using a username and password. It is accessible either by an administrator or receptionist. Only they can add data into the database. The data can be retrieved easily. The interface is very user-friendly. The data are well protected for personal use and makes the data processing very fast. Hospital Management System is powerful, flexible, and easy to use and is designed and developed to deliver real conceivable benefits to hospitals. Hospital Management System is designed for multispecialty hospitals, to cover a wide range of hospital administration and management processes. It is an integrated end-to-end Hospital Management System that provides relevant information across the hospital to support effective decision making for patient care, hospital administration and critical financial accounting, in a seamless flow. Hospital Management System is a software product suite designed to improve the quality and management of hospital management in the areas of clinical process analysis and activity-based costing. Hospital Management System enables you to develop your organization and improve its effectiveness and quality of work. Managing the key processes efficiently is critical to the success of the hospital helps you manage your processes.

Hospital management system project report.pdf

Kamal Acharya

Danikor Product Catalog- Screw Feeder.pdf

thietkevietthinh

Augmented Reality (AR) with Augin Software.pptx

Mustafa Ahmed

Introduction to Artificial Intelligence ( AI)

ChandrakantDivate1

In the dynamic landscape of energy storage, choosing the right battery pack is a critical decision that significantly impacts performance, efficiency, and overall product design. This webinar aims to unravel the complexities surrounding standard and custom battery packs (primarily lithium), providing you with a comprehensive understanding to make informed decisions. We'll embark on a journey to explore the fundamental distinctions between standard off-the-shelf battery packs and their bespoke counterparts tailored to specific applications. We will delve into the nuance of what is defined as a standard battery pack along with its benefits, limitations, and how it caters to broad market needs. Simultaneously, we will dissect the world of custom battery packs, diving into the advantages they offer in terms of precise energy requirements, form factors, and unique design considerations. Whether you are a product designer, engineer, or industry professional, this webinar is designed to equip you with the knowledge necessary to navigate the intricate terrain of battery pack selection. Join us for this webinar as we navigate through the intricacies of standard and custom battery packs, empowering you to make strategic decisions that align with your project goal. For more information on our battery pack solutions, visit https://www.epectec.com/batteries/.

Standard vs Custom Battery Packs - Decoding the Power Play

Epec Engineered Technologies

Computer Networks Basics of Network Devices

ChandrakantDivate1

Microprocessors & Microcontrollers: Interrupt controller 8259; The 8259 is known as the Programmable Interrupt Controller (PIC) microprocessor. In 8085 and 8086 there are five hardware interrupts and two hardware interrupts respectively. Bu adding 8259, we can increase the interrupt handling capability. This chip combines the multi-interrupt input source to single interrupt output. This provides 8-interrupts from IR0 to IR7. Let us see some features of this microprocessor. This chip is designed for 8085 and 8086. It can be programmed either in edge triggered, or in level triggered mode We can mask individual bits of Interrupt Request Register. By cascading 8259 chips, we can increase interrupts up to 64 interrupt lines Clock cycle is not needed. 8259 microprocessor can be programmed according to given interrupts condition and it can be provided either with level or edge-triggered interrupt level. It can be programmed to either work in 8085 or in 8086 microprocessors. Individual interrupt bits can be masked. By cascading Nine 8259’s in Master-Slave Configuration we can handle up to 64 interrupt pins. It contains 3 registers commonly known as ISR, IRR, IMR & there is 1 priority resolver (PR). Interrupt Request Register (IRR): It stores those bits which are requested for their interrupt services. Interrupt Service Register (ISR): It stores the interrupt levels which is currently being served. Interrupt Mask Register (IMR): It stores interrupt levels that have to be masked. These interrupt levels are already accepted by the 8259 microprocessor. Priority Resolver (PR): It examines all the 3 registers and sets the priority of interrupts and sets the interrupt levels in ISR which has the highest priority and the rest of the interrupt bit is IRR which is already accepted. SP/EN (low active pin): If its value is 1 it works in master mode & if its value=e is 0 then it works in slave mode. Cascade Buffer: It is used to cascade more number of Programmable Interrupt Controller to increase the interrupts handling capability up to 64 levels. Advantages: Interrupt management: The 8259 microprocessor is a specialized chip that is dedicated to managing interrupts, which can help to improve system performance and reduce the workload on the main CPU. Programmability: The 8259 microprocessor is programmable, which means that it can be customized to handle specific types of interrupts and to prioritize different interrupt requests. Compatibility: The 8259 microprocessor is compatible with a wide range of microprocessors, making it a popular choice for interrupt management in many different systems. Multiple interrupt inputs: The 8259 microprocessor can handle multiple interrupt inputs, which makes it a useful peripheral for managing complex systems with multiple devices. Ease of use: The 8259 microprocessor includes simple interface pins and registers, making it relatively easy to use and program.

INTERRUPT CONTROLLER 8259 MICROPROCESSOR

TanishkaHira1

Theory of Time 2024 (Universal Theory for Everything)

Ramkumar k

8th International Conference on Soft Computing, Mathematics and Control (SMC 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications impacts and challenges of Soft Computing, Mathematics and Control. The conference documents practical and theoretical results which make a fundamental contribution for the development of Soft Computing, Mathematics and Control. The aim of the conference is to provide a platform to the researchers and practitioners from both academia as well as industry to meet and share cutting-edge development in the field.

8th International Conference on Soft Computing, Mathematics and Control (SMC ...

josephjonse

S1S2 B.Arch MGU - HOA1&2 Module 3 -Temple Architecture of Kerala.pptx

SCMS School of Architecture

Path loss model, OKUMURA Model, Hata Model

DrAjayKumarYadav4

Design For Accessibility: Getting it right from the start

Quintin Balsdon

Robotics is an exciting and rapidly evolving field that combines engineering, computer science, and technology. Robots are versatile machines capable of automating a wide range of tasks with precision, speed, and power. This introduction will explore the history, components, and applications of this transformative technology. A robot is an electromechanical machine designed to perform tasks autonomously or semi-autonomously. Robots are controlled by sophisticated software and can be programmed to carry out a variety of functions. Robots use sensors to gather information about their environment and actuators to interact with it. The concept of automata, or self-operating machines, dates back to ancient civilizations such as Greece and China. The development of industrial machinery in the 18th and 19th centuries paved the way for modern robotics. The advent of computers in the 20th century enabled the creation of more advanced, programmable robots Industrial robots are commonly used in manufacturing, performing tasks such as welding, painting, and assembly. Service robots are designed to assist humans in tasks like cleaning, transportation, and healthcare Humanoid robots are designed to mimic the appearance and functionality of the human body. Mechanical Structure Robots have a physical frame, joints, and linkages that allow for movement and manipulation. Power Source Robots require a power source, such as batteries or electrical connections, to operate. Control System The control system, often a computer or microcontroller, coordinates the robot's actions and movements End Effectors Robots may have specialized tools, such as grippers or tools, to interact with their environment Cameras and other vision sensors allow robots to perceive and respond to their environment. Ultrasonic and infrared sensors help robots detect and avoid obstacles. Tactile sensors enable robots to feel and interact with their surroundings. Motors, servos, and other actuators allow robots to move and manipulate objects. Robots are controlled by sophisticated software that defines their behaviors and decision-making processes. Algorithms enable robots to process sensor data, plan actions, and execute tasks autonomously. Advancements in machine learning allow robots to adapt and improve their performance over time. Applications of Robotics Robots are widely used in industrial settings for tasks such as assembly, welding, and packaging. Robotic systems are revolutionizing medicine, from surgical assistants to rehabilitation devices. Robots are essential for exploring hazardous environments, such as deep-sea and outer space

Introduction to Robotics in Mechanical Engineering.pptx

hublikarsn

Basic Electronics for diploma students as per technical education Kerala Syll...

ppkakm

Although the Dark web was originally used for maintaining privacy-sensitive communication for business or intelligence services for defence, government and business organizations, fighting against censorship and blocked content, later, the advantage of technologies behind the Dark web were abused by criminals to conduct crimes which involve drug dealing to the contract of assassinations in a widespread manner. Since the communication remains secure and untraceable, criminals can easily use dark web service via The Onion Router (TOR), can hide their illegal motives and can conceal their criminal activities. This makes it very difficult to monitor and detect cybercrimes over the dark web. With the evolution of machine learning, natural language processing techniques, computational big data applications and hardware, there is a growing interest in exploiting dark web data to monitor and detect criminal activities. Due to the anonymity provided by the Dark Web, the rapid disappearance and the change of the uniform resource locators (URLs) of the resources, it is not as easy to crawl the Drak web and get the data as the usual surface web which limits the researchers and law enforcement agencies to analyse the data. Therefore, there is an urgent need to study the technology behind the Dark web, its widespread abuse, its impact on society and the existing systems, to identify the sources of drug deal or terrorism activities. In this research, we analysed the predominant darker sides of the world wide web (WWW), their volumes, their contents and their ratios. We have performed the analysis of the larger malicious or hidden activities that occupy the major portions of the Dark net; tools and techniques used to identify cybercrimes which happen inside the dark web. We applied a systematic literature review (SLR) approach on the resources where the actual dark net data have been used for research purposes in several areas. From this SLR, we identified the approaches (tools and algorithms) which have been applied to analyse the Dark net data, the key gaps as well as the key contributions of the existing works in the literature. In our study, we find the main challenges to crawl the dark web and collect forum data are: scalability of crawler, content selection trade off, and social obligation for TOR crawler and the limitations of techniques used in automatic sentiment analysis to understand criminals’ forums and thereby monitor the forums. From the comprehensive analysis of existing tools, our study summarizes the most tools. However the forum topics rapidly change as their sources changes; criminals inject noises to obfuscate the forum’s main topic and thus remain undetectable. Therefore supervised techniques fail to address the above challenges. Semi-supervised techniques would be an interesting research direction.

Cybercrimes in the Darknet and Their Detections: A Comprehensive Analysis and...

dannyijwest

Computer Graphics Introduction To Curves

ChandrakantDivate1

Fundamentals of Internet of Things (IoT) Part-2

ChandrakantDivate1

Adsorption (mass transfer operations 2) ppt

jigup7320

In this relay, the armature is attracted to the pole of a magnet. The electromagnetic force exerted on the moving element is proportional to the square of the current flow through the coil. This relay responds to both the alternating and direct current. For AC quantity the electromagnetic force developed is given as equation-1The above equation shows that the electromagnetic relay consists two components, one constant independent of time and another dependent upon time and pulsating at double supply frequency. This double supply frequency produces noise and hence damage the relay contacts. The difficulty of a double frequency supply is overcome by splitting the flux developing in the electromagnetic relay. These fluxes were acting simultaneously but differ in time phase. Thus the resulting deflecting force is always positive and constant. The splitting of fluxes is achieved by using the electromagnet having a phase shifting networks or by putting shading rings on the poles of an electromagnet. The electromagnetic attraction relay is the simplest type of relay which includes a plunger (or solenoid), hinged armature, rotating armature (or balanced) and moving iron polarised relay.

Electromagnetic relays used for power system .pptx

NANDHAKUMARA10

Databricks Generative AI Fundamentals .pdf

VinayVadlagattu

Recently uploaded (20)

Hospital management system project report.pdf

Danikor Product Catalog- Screw Feeder.pdf

Augmented Reality (AR) with Augin Software.pptx

Introduction to Artificial Intelligence ( AI)

Standard vs Custom Battery Packs - Decoding the Power Play

Computer Networks Basics of Network Devices

INTERRUPT CONTROLLER 8259 MICROPROCESSOR

Theory of Time 2024 (Universal Theory for Everything)

8th International Conference on Soft Computing, Mathematics and Control (SMC ...

S1S2 B.Arch MGU - HOA1&2 Module 3 -Temple Architecture of Kerala.pptx

Path loss model, OKUMURA Model, Hata Model

Design For Accessibility: Getting it right from the start

Introduction to Robotics in Mechanical Engineering.pptx

Basic Electronics for diploma students as per technical education Kerala Syll...

Cybercrimes in the Darknet and Their Detections: A Comprehensive Analysis and...

Computer Graphics Introduction To Curves

Fundamentals of Internet of Things (IoT) Part-2

Adsorption (mass transfer operations 2) ppt

Electromagnetic relays used for power system .pptx

Databricks Generative AI Fundamentals .pdf

Evolving multi level graph

1. EVOLVING MULTI-LEVEL GRAPH PARTITIONING ALGORITHMS Master : Ali Jahanian Student : Mohammad Rostami

2. Introduction Graph Partitioning Evolutionary Computation Genetic Programming Review Primitive Operation Set Experiment Results Conclusion Future Work

3.  organizing parallel computation workload VLSI layout design image processing critical infrastructure protection optimal graph partitioning is known to be NP-hard Introduction

4. Graph Partitioning

5.  The total weight for a set of vertices X is given by: the total weight of a set of edges Y is: A balanced partition requires that: Graph Partitioning

6. Coarsening:  Random matching  Light edge matching  Heavy edge matching Partition  Graph growing partition (GGP)  Greedy graph growing partition (GGGP) Multi-level Graph Partitioning

7. Uncoarsening and Refinement:  KL refinement  Greedy refinement Multi-level Graph Partitioning

8. Initialization Parent selection Recombination Mutation Survival selection Termination Evolutionary Computation

9. Evolutionary Computation

10. Methodology

11. Methodology

12. Primitive Operation Set

13. Erdos-R ¨ enyi Barab ´ asi- ´ Albert Los Alamos National Laboratory (LANL) One month of the network data set was modeled as a bipartite graph with 9,924 user vertices, 14,822 computer vertices, and 106,693 authentication edges. Subgraphs were created by inducing the set of vertices visited by a random walk of the total graph. Experiment

14. Results

15. Results

16. Results

17. Results

18. Although there are a variety of multi-level partitioning algorithms available, most are intended to serve as general purpose solutions. An obvious continuation of this work would be to include the consideration of execution time. Instead of focusing on reducing the cost of the partitions produced, evolved partitioners could instead aim to reduce the time needed to partition certain types of graphs for time-sensitive applications. Conclusion & Future Work

19. Question ?

Evolving multi level graph

Recommended

Recommended

More Related Content

Similar to Evolving multi level graph

Similar to Evolving multi level graph (20)

Recently uploaded

Recently uploaded (20)

Evolving multi level graph