DOCSLIB.ORG

Review Article Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Review Article Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis Hindawi Advances in Bioinformatics Volume 2017, Article ID 1278932, 8 pages https://doi.org/10.1155/2017/1278932 Review Article Empirical Comparison of Visualization Tools for Larger-Scale Network Analysis Georgios A. Pavlopoulos,1 David Paez-Espino,1 Nikos C. Kyrpides,1 and Ioannis Iliopoulos2 1 Department of Energy, Joint Genome Institute, Lawrence Berkeley Labs, 2800 Mitchell Drive, Walnut Creek, CA 94598, USA 2Division of Basic Sciences, University of Crete Medical School, Andrea Kalokerinou Street, Heraklion, Greece Correspondence should be addressed to Georgios A. Pavlopoulos; [email protected] and Ioannis Iliopoulos; [email protected] Received 22 February 2017; Revised 14 May 2017; Accepted 4 June 2017; Published 18 July 2017 Academic Editor: Klaus Jung Copyright © 2017 Georgios A. Pavlopoulos et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today’s indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. We comment on their strengths and their weaknesses and empirically discuss their scalability, user friendliness, and postvisualization capabilities. 1. Background the available tools, and spot the strengths and the weaknesses of a tool of interest at a glance, no empirical feedback on the Health and natural sciences have become protagonists in the tools’ scalability was obvious. big-data world as high-throughput advances continuously To shortly mention representative tools in the field, 2D contribute to the exponential growth of data volumes. Nowa- standalone applications like graphVizdb [7], Ondex [8], days, biological repositories expand every day by hosting Proviz [9], VizANT [10], GUESS [11], UCINET [12], MAP- various entities such as proteins, genes, drugs, chemicals, MAN [13], PATIKA [14], Medusa [15], or Osprey [16] as ontologies, functions, articles, and the interactions between well as 3D visualization tools such as Arena3D [17, 18] them, often leading to large-scale networks of thousands or and BioLayout Express [19] already exist. Each of them is even millions of nodes and connections. As such networks are designed to serve a different purpose. For example, Ondex characterized by different properties and topologies, graph is implemented to gather and manage data from diverse and theory comes to play a very important role by providing ways heterogeneous datasets, Proviz is dedicated to handle protein- to efficiently store, analyze, and subsequently visualize them protein interaction datasets, VizANT focuses on metabolic [1–5]. networks and ecosystems, Medusa is able to show semantic Visualization and exploration of biological networks at networks and multiedged connections, GUESS supports such scale are a computationally challenging task and many dynamic and time sensitive data, Osprey is implemented to efforts in this direction have failed over the years. Recent annotate biological networks, Arena3D is targeting multilay- reviewarticles[3,4,6]discussthechallengesinthebiological ered graphs, and BioLayout Express is designed for generic data visualization field and list a catalog of standalone and advanced 3D network visualizations. web-basedvisualizationtoolsaswellasthevisualconcepts Despite the fact that such tools are widely used and have they are implemented to serve. While these resources are great potential for further development, to our experience, valuable to capture the big picture in the field, get a sense of they are not recommended for large-scale network analysis in 2 Advances in Bioinformatics their current versions. UCINET windows application could CPU and memory greedy by requiring long running time potentiallybeusedforjustvisualizationpurposes.Itsabsolute to be completed. While Gephi comes with a great variety of maximum network size is about 2 million nodes but, in layout algorithms, OpenOrd [26] and Yifan-Hu [27] force- practice, most of its procedures are too slow to run networks directed algorithms are mostly recommended for large-scale larger than about 5,000 nodes. network visualization. OpenOrd, for example, can scale up to Among several existing tools that we tested, we find over a million nodes in less than half an hour while Yifan-Hu Cytoscape (v3.5.1) [20], Tulip (v4.10.0) [21], Gephi (v0.9.1) is an ideal option to apply after the OpenOrd layout. Notably, [22], and Pajek (v5.01) [23, 24] standalone applications to Yifan-Hu layout can give aesthetically comparable views be the top four candidates for visualization, manipulation, totheonesproducedbythewidelyusedbutconservative exploration, and analysis of very big networks. For these and time-consuming Fruchterman and Reingold [28]. Other four tools, we empirically evaluate their pros and cons, we algorithms offered by Gephi are the circular, contraction, dual comment on their scalability, user friendliness, layout speed, circle, random, MDS, Geo, Isometric, GraphViz, and Force offered analyses, profiling, memory efficiency, and visual atlas layouts. While most of them can run in an affordable styles, and we provide tips and advice on which of their running time, the combination of OpenOrd and Yifan-Hu features can scale and which of them is better to avoid. seems to give the most appealing visualizations. Descent visualization is also offered by OpenOrd layout algorithm if In order to show a representative visualization generated auserstopstheprocesswhen∼50–60% of the progress has by these four tools, we constructed a graph consisting been completed. Of course, efficient parametrization of any of 202,424 nodes and 354,468 edges showing the habitat chosen layout algorithm will affect both the running time and distribution of 202,417 protein families across 7 habitats. the visual result. Data was collected from the IMG integrated genome and metagenome comparative data analysis system [25] whereas 2.1.3. Postvisualization Analysis. Edge-bundling and famous protein families originate from public metagenomes only. clustering algorithms such as the MCL [29] do not come A step-by-step protocol describing how these images were by default with Gephi but can be downloaded from Gephi’s generated is presented as Supplementary Material, available plugin library (∼100 plugins). In addition, GeoLayout Gephi’s online at https://doi.org/10.1155/2017/1278932. Comments on plugin is very suitable to plot a network with geographical problems which occurred during our analysis as well as information. Coming to dynamic network visualization, drawbacks and strengths of the visualization tools used for Gephi is the forefront of innovation with dynamic graph the purposes of this review are extensively discussed. analysis. Users can visualize how a network evolves over time by manipulating its embedded timeline. While visualization 2. Top Four Candidates for Large-Scale of a network over time is something very useful, its current algorithms are not suitable for large-scale networks. Similarly, Network Visualization for large-scale networks it is highly recommended for users 2.1. Gephi (Version 0.9.1). Gephiisfreeopen-source,lead- to apply clustering algorithms using external command line ing visualization and exploration software for all kinds of applications and then import the clustering results to a networksandrunsonWindows,MacOSX,andLinux.It visualization tool. is our top preference as it is highly interactive and users To study a network’s topology, Gephi comes with a can easily edit the node/edge shapes and colors to reveal very basic but high quality network profiler showing basic hidden patterns. The aim of the tools is to assist users in statistics about the network such as the number of nodes, pattern discovery and hypothesis making through efficient the number of edges, its density, its clustering coefficient, dynamic filtering and iterative visualization routines. As a and other metrics. Automatically calculated node attributes generic tool, it is applicable to exploratory data analysis, link such as node connectivity, clustering coefficient, betweenness analysis, social network analysis, biological network analysis, centrality, or edge weight and similarly are trivial tasks and do and poster creation. not require long time to be calculated. 2.1.1. Scalability. Gephi comes with a very fast rendering 2.1.4. Editing. Gephi is highly interactive and provides clever engine and sophisticated data structures for object handling, shortcuts to highlight communities, and shortest paths or thus making it one of the most suitable tools for large- relative distances of any node to a node of interest are offered. scale network visualization. It offers very highly appealing Moreover, users can easily adjust or interactively filter the visualizations and, in a typical computer, it can easily ren- shapes and colors of the network’s edges and nodes according der networks up to
Load more

Recommended publications
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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…
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]