Review Article Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis
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Gephi Tools for Network Analysis and Visualization
Frontiers of Network Science Fall 2018 Class 8: Introduction to Gephi Tools for network analysis and visualization Boleslaw Szymanski CLASS PLAN Main Topics • Overview of tools for network analysis and visualization • Installing and using Gephi • Gephi hands-on labs Frontiers of Network Science: Introduction to Gephi 2018 2 TOOLS OVERVIEW (LISTED ALPHABETICALLY) Tools for network analysis and visualization • Computing model and interface – Desktop GUI applications – API/code libraries, Web services – Web GUI front-ends (cloud, distributed, HPC) • Extensibility model – Only by the original developers – By other users/developers (add-ins, modules, additional packages, etc.) • Source availability model – Open-source – Closed-source • Business model – Free of charge – Commercial Frontiers of Network Science: Introduction to Gephi 2018 3 TOOLS CINET CyberInfrastructure for NETwork science • Accessed via a Web-based portal (http://cinet.vbi.vt.edu/granite/granite.html) • Supported by grants, no charge for end users • Aims to provide researchers, analysts, and educators interested in Network Science with an easy-to-use cyber-environment that is accessible from their desktop and integrates into their daily work • Users can contribute new networks, data, algorithms, hardware, and research results • Primarily for research, teaching, and collaboration • No programming experience is required Frontiers of Network Science: Introduction to Gephi 2018 4 TOOLS Cytoscape Network Data Integration, Analysis, and Visualization • A standalone GUI application -
Performing Knowledge Requirements Analysis for Public Organisations in a Virtual Learning Environment: a Social Network Analysis
chnology Te & n S o o ti ft a w a m r r e Fontenele et al., J Inform Tech Softw Eng 2014, 4:2 o f E Journal of n n I g f i o n DOI: 10.4172/2165-7866.1000134 l e a e n r r i n u g o J ISSN: 2165-7866 Information Technology & Software Engineering Research Article Open Access Performing Knowledge Requirements Analysis for Public Organisations in a Virtual Learning Environment: A Social Network Analysis Approach Fontenele MP1*, Sampaio RB2, da Silva AIB2, Fernandes JHC2 and Sun L1 1University of Reading, School of Systems Engineering, Whiteknights, Reading, RG6 6AH, United Kingdom 2Universidade de Brasília, Campus Universitário Darcy Ribeiro, Brasília, DF, 70910-900, Brasil Abstract This paper describes an application of Social Network Analysis methods for identification of knowledge demands in public organisations. Affiliation networks established in a postgraduate programme were analysed. The course was ex- ecuted in a distance education mode and its students worked on public agencies. Relations established among course participants were mediated through a virtual learning environment using Moodle. Data available in Moodle may be ex- tracted using knowledge discovery in databases techniques. Potential degrees of closeness existing among different organisations and among researched subjects were assessed. This suggests how organisations could cooperate for knowledge management and also how to identify their common interests. The study points out that closeness among organisations and research topics may be assessed through affiliation networks. This opens up opportunities for ap- plying knowledge management between organisations and creating communities of practice. -
A Comparative Analysis of Large-Scale Network Visualization Tools
A Comparative Analysis of Large-scale Network Visualization Tools Md Abdul Motaleb Faysal and Shaikh Arifuzzaman Department of Computer Science, University of New Orleans New Orleans, LA 70148, USA. Email: [email protected], [email protected] Abstract—Network (Graph) is a powerful abstraction for scalability for large network analysis. Such study will help a representing underlying relations and structures in large complex person to decide which tool to use for a specific case based systems. Network visualization provides a convenient way to ex- on his need. plore and study such structures and reveal useful insights. There exist several network visualization tools; however, these vary in One such study in [3] compares a couple of tools based on terms of scalability, analytics feature, and user-friendliness. Due scalability. Comparative studies on other visualization metrics to the huge growth of social, biological, and other scientific data, should be conducted to let end users have freedom in choosing the corresponding network data is also large. Visualizing such the specific tool he needs to use. In this paper, we have chosen large network poses another level of difficulty. In this paper, we five widely used visualization tools for an empirical and identify several popular network visualization tools and provide a comparative analysis based on the features and operations comparative analysis. Our comparisons are based on factors these tools support. We demonstrate empirically how those tools such as supported file formats, scalability in visualization scale to large networks. We also provide several case studies of and analysis, interacting capability with the drawn network, visual analytics on large network data and assess performances end user-friendliness (e.g., users with no programming back- of the tools. -
Week 2 - Handling Real-World Network Datasets
28/12/2017 Social Networks - - Unit 6 - Week 2 - Handling Real-world Network Datasets X [email protected] ▼ Courses » Social Networks Unit 6 - Week Announcements Course Forum Progress Mentor 2 - Handling Real-world Network Datasets Course outline Week2-assignment1 Course Trailer . FAQ 1) Citation Network is which type of network? 1 point Things to Note Directed Accessing the Portal Undirected Week 1 - Introduction Accepted Answers: Week 2 - Handling Real-world Network Directed Datasets 2) Co-authorship Network is which type of network? 1 point Directed Undirected Lecture 14 - Introduction to Datasets Accepted Answers: Undirected 3) What is the full form of CSV? 1 point Computer Systematic Version Comma Separated Version Comma Separated Values Computer Systematic Values Lecture 15 - Ingredients Network Accepted Answers: Comma Separated Values 4) Which of the following is True with respect to the function read_weighted_edgelist() 1 point This function can be used to read a weighted network dataset, however, the weights should only be in string format. Lecture 16 - This function can be used to read a weighted network dataset, however, the weights should only be numeric. Synonymy Network This function can be used to read a weighted network dataset and the weights can be in any format. The statement is wrong; such a function does not exist. Accepted Answers: This function can be used to read a weighted network dataset, however, the weights should only be numeric. Lecture 17 - Web 5) Choose the one that is False out of the following: 1 point Graph GML stands for Graph Modeling Language. https://onlinecourses.nptel.ac.in/noc17_cs41/unit?unit=42&assessment=47 1/4 28/12/2017 Social Networks - - Unit 6 - Week 2 - Handling Real-world Network Datasets GML stores the data in the form of tags just like XML. -
Networkx: Network Analysis with Python
NetworkX: Network Analysis with Python Dionysis Manousakas (special thanks to Petko Georgiev, Tassos Noulas and Salvatore Scellato) Social and Technological Network Data Analytics Computer Laboratory, University of Cambridge February 2017 Outline 1. Introduction to NetworkX 2. Getting started with Python and NetworkX 3. Basic network analysis 4. Writing your own code 5. Ready for your own analysis! 2 1. Introduction to NetworkX 3 Introduction: networks are everywhere… Social Mobile phone networks Vehicular flows networks Web pages/citations Internet routing How can we analyse these networks? Python + NetworkX 4 Introduction: why Python? Python is an interpreted, general-purpose high-level programming language whose design philosophy emphasises code readability + - Clear syntax, indentation Can be slow Multiple programming paradigms Beware when you are Dynamic typing, late binding analysing very large Multiple data types and structures networks Strong on-line community Rich documentation Numerous libraries Expressive features Fast prototyping 5 Introduction: Python’s Holy Trinity Python’s primary NumPy is an extension to Matplotlib is the library for include primary plotting mathematical and multidimensional arrays library in Python. statistical computing. and matrices. Contains toolboxes Supports 2-D and 3-D for: Both SciPy and NumPy plotting. All plots are • Numeric rely on the C library highly customisable optimization LAPACK for very fast and ready for implementation. professional • Signal processing publication. • Statistics, and more… -
Using Computer Games Techniques for Improving Graph Visualization Efficiency
Poster Abstracts at Eurographics/ IEEE-VGTC Symposium on Visualization (2010) Using Computer Games Techniques for Improving Graph Visualization Efficiency Mathieu Bastian1, Sebastien Heymann1;2 and Mathieu Jacomy3 1INIST-CNRS, France 2IMS CNRS - ENSC Bordeaux, France 3WebAtlas, Telecom ParisTech, France Abstract Creating an efficient, interactive and flexible unified graph visualization system is a difficult problem. We present a hardware accelerated OpenGL graph drawing engine, in conjunction with a flexible preview package. While the interactive OpenGL visualization focuses on performance, the preview focuses on aesthetics and simple network map creation. The system is implemented as Gephi, a modular and extensible open-source Java application built on top of the Netbeans Platform, currently in alpha version 0.7. Categories and Subject Descriptors (according to ACM CCS): H.5.2 [Information Systems]: User Interfaces—I.3.6 [Computing Methodologies]: Methodology and Techniques—Interaction techniques 1. Introduction For guaranteeing best performance, the 3-D visualization engine leaves apart effects and complex shapes, for instance Large scale graph drawing remains a technological chal- curved edges and focus on basics, i.e. nodes, edges and la- lenge, actively developed by the infovis community. Many bels. As it is specialized in a particular task, the visual- systems have successfully brought interactive graph visu- ization engine is thus less flexible. That’s why a comple- alization [Aub03], [Ada06], [TAS09] but still have visu- mentary drawing library, named Preview is needed to bring alization limitations to render large graphs at some mo- parametrization and flexibility. The following sections de- ments. Various techniques have been used to either optimize scibe the role granted to each visualization module. -
Mastering Gephi Network Visualization
Mastering Gephi Network Visualization Produce advanced network graphs in Gephi and gain valuable insights into your network datasets Ken Cherven BIRMINGHAM - MUMBAI Mastering Gephi Network Visualization Copyright © 2015 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information. First published: January 2015 Production reference: 1220115 Published by Packt Publishing Ltd. Livery Place 35 Livery Street Birmingham B3 2PB, UK. ISBN 978-1-78398-734-4 www.packtpub.com Credits Author Project Coordinator Ken Cherven Leena Purkait Reviewers Proofreaders Ladan Doroud Cathy Cumberlidge Miro Marchi Paul Hindle David Edward Polley Samantha Lyon Mollie Taylor George G. Vega Yon Indexer Monica Ajmera Mehta Commissioning Editor Ashwin Nair Graphics Abhinash Sahu Acquisition Editor Sam Wood Production Coordinator Conidon Miranda Content Development Editor Amey Varangaonkar Cover Work Conidon Miranda Technical Editors Shruti Rawool Shali Sasidharan Copy Editors Rashmi Sawant Stuti Srivastava Neha Vyas About the Author Ken Cherven is a Detroit-based data visualization and open source enthusiast, with 20 years of experience working with data and visualization tools. -
Network Science and Python
Network Analysis using Python Muhammad Qasim Pasta Assistant Professor Usman Institute of Technology About Me • Assistant Professor @ UIT • Research Interest: Network Science / Social Network Analysis • Consultant / Mentor • Python Evangelist • Github: https://github.com/mqpasta • https://github.com/mqpasta/pyconpk2018 • Website: http://qasimpasta.info Today’s Talk What is a Network? • An abstract representation to model pairwise relationship between objects. • Network = graph: mathematical representation nodes/vertices/actors edges/arcs/lines Birth of Graph Theory • Königsberg bridge problem: the seven bridges of the city of Königsberg over the river Preger • traversed in a single trip without doubling back? • trip ends in the same place it began Solution as graph! • Swiss mathematician - Leonhard Euler • considering each piece of island as dot • each bridge connecting two island as a line between two dots • a graph of dots (vertices or nodes) and lines (edges or links). Background | Literature Review | Questions | Contributions | Conclusion | Q/A Network Science • ―Network science is an academic field which studies complex networks such as telecommunication networks, computer networks …‖ [Wikipedia] • ―In the context of network theory, a complex network is a graph (network) with non- trivial topological features—features that do not occur in simple networks …‖ [Wikipedia] Study of Network Science Social Graph structure theory Inferential Statistical modeling mechanics Information Data Mining Visualization Background | Literature Review -
An XML-Based Description of Structured Networks
Massimo Ancona, Walter Cazzola, Sara Drago, and Francesco Guido. An XML-Based Description of Structured Networks. In Proceedings of Interna- tional Conference Communications 2004, pages 401–406, Bucharest, Romania, June 2004. IEEE Press. An XML-Based Description of Structured Networks Massimo Ancona£ Walter Cazzola† Sara Drago£ Francesco Guido‡ £ DISI University of Genova, e-mail: fancona,[email protected] † DICo University of Milano, e-mail: [email protected] ‡ Marconi Selenia Communication, e-mail: [email protected] Abstract In this paper we present an XML-based formalism to describe hierarchically organized communication networks. After a short overview of existing graph description languages, we discuss the advantages and disadvantages of their application in network optimization. We conclude by extending one of these formalisms with features supporting the description of the relationship between the optimized logical layout of a network and its physical counterpart. Elements for describing traffic parameters are also given. 1 Introduction The problem of network design and optimization has a strategical importance especially for military networks. In this case, design for fault tolerance and security plays a role more and more crucial than the equivalent importance required for commercial and research networks. The reengineering of a large communication network is a complex problem, which consists of different aspects that can be strongly affected by the way of describing data. The plainest way to describe a communication network is to model the relationship among sites and links by means of a weighted undirected graph, but unless some more assumptions are taken, this approach can raise several problems. A first issue is encountered when an analysis and a visualization of the network has to be realized: practical networks include hundreds or often thousands of nodes and links, so that even a simple description and documentation of the network structure is hard to maintain and update. -
Software Tools for Complex Networks Analysis
Software tools for Complex Networks Analysis Fabrice Huet, University of Nice Sophia- Antipolis SCALE Team MOTIVATION Why do we need tools ? Source : nature.com • Visualization • Properties extraction • Complex queries Source : Boldi et al. Graphs are everywhere • RDF (“test1”, writtenBy, “Sophie”) (“test1”, publishedIn, “Journal”) (“test2”, publishedIn, “Journal) • SPARQL SELECT ?s WHERE { ?s writtenBy ?a. ?a hasName “Sophie”. ?s publishedIn “Journal”. } • Basically a sub-graph matching Why are graphs different ? • Graphs can be large - Facebook : 720M users, 69B friends in 2011 - Twitter : 537M accounts, 23.95B links in 2012 • Low memory cost per vertex - 1 ID, 1 pointer/edge • Low computation per vertex • Graphs are not memory friendly - Random jumps to memory • They are not hardware friendly! Lots of frameworks • Really lots of them - Matlab, NetworkX, GraphChi, Hadoop, Twister, Piccolo, Maiter, Pregel, Giraph, Hama, GraphLab, Pegasus, Snap, Neo4J, Gephi, Tulip, any DBMS,… • Why so many ? - Not one size fits all - Different computational models - Different architecture Possible taxonomy • Generic vs Specialized - Hadoop vs GraphChi (or Giraph, GraphX…) • Shared vs Distributed Memory - GraphChi vs Pregel • Synchronous vs Asynchronous - Giraph vs Maiter • Single vs Multi threaded - NetworkX vs GraphiChi NETWORKX 8 Overview • A Python package for complex network analysis • Simple API • Very flexible - Can attach any data to vertices and edges - Supports visualization • Graphs generators • http://networkx.github.io/ Dependencies • Supports -
The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing
The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing Siddhartha Sahu, Amine Mhedhbi, Semih Salihoglu, Jimmy Lin, M. Tamer Özsu David R. Cheriton School of Computer Science University of Waterloo {s3sahu,amine.mhedhbi,semih.salihoglu,jimmylin,tamer.ozsu}@uwaterloo.ca ABSTRACT 52, 55], and distributed graph processing systems [17, 21, 27]. In Graph processing is becoming increasingly prevalent across many the academic literature, a large number of publications that study application domains. In spite of this prevalence, there is little re- numerous topics related to graph processing regularly appear across search about how graphs are actually used in practice. We conducted a wide spectrum of research venues. an online survey aimed at understanding: (i) the types of graphs Despite their prevalence, there is little research on how graph data users have; (ii) the graph computations users run; (iii) the types is actually used in practice and the major challenges facing users of graph software users use; and (iv) the major challenges users of graph data, both in industry and research. In April 2017, we face when processing their graphs. We describe the participants’ conducted an online survey across 89 users of 22 different software responses to our questions highlighting common patterns and chal- products, with the goal of answering 4 high-level questions: lenges. We further reviewed user feedback in the mailing lists, bug (i) What types of graph data do users have? reports, and feature requests in the source repositories of a large (ii) What computations do users run on their graphs? suite of software products for processing graphs. -
67 SOFTWARE Michael Joswig and Benjamin Lorenz
67 SOFTWARE Michael Joswig and Benjamin Lorenz INTRODUCTION This chapter is intended as a guide through the ever-growing jungle of geometry software. Software comes in many guises. There are the fully fledged systems consisting of a hundred thousand lines of code that meet professional standards in software design and user support. But there are also the tiny code fragments scattered over the Internet that can nonetheless be valuable for research purposes. And, of course, the very many individual programs and packages in between. Likewise today we find a wide group of users of geometry software. On the one hand, there are researchers in geometry, teachers, and their students. On the other hand, geometry software has found its way into numerous applications in the sciences as well as industry. Because it seems impossible to cover every possible aspect, we focus on software packages that are interesting from the researcher's point of view, and, to a lesser extent, from the student's. This bias has a few implications. Most of the packages listed are designed to run on UNIX/Linux1 machines. Moreover, the researcher's genuine desire to understand produces a natural inclination toward open source software. This is clearly reflected in the selections below. Major exceptions to these rules of thumb will be mentioned explicitly. In order to keep the (already long) list of references as short as possible, in most cases only the Web address of each software package is listed rather than manuals and printed descriptions, which can be found elsewhere. At least for the freely available packages, this allows one to access the software directly.