Gephi, Nodexl, Pajek) Social Network Analysis Tools
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Networkx Tutorial
5.03.2020 tutorial NetworkX tutorial Source: https://github.com/networkx/notebooks (https://github.com/networkx/notebooks) Minor corrections: JS, 27.02.2019 Creating a graph Create an empty graph with no nodes and no edges. In [1]: import networkx as nx In [2]: G = nx.Graph() By definition, a Graph is a collection of nodes (vertices) along with identified pairs of nodes (called edges, links, etc). In NetworkX, nodes can be any hashable object e.g. a text string, an image, an XML object, another Graph, a customized node object, etc. (Note: Python's None object should not be used as a node as it determines whether optional function arguments have been assigned in many functions.) Nodes The graph G can be grown in several ways. NetworkX includes many graph generator functions and facilities to read and write graphs in many formats. To get started though we'll look at simple manipulations. You can add one node at a time, In [3]: G.add_node(1) add a list of nodes, In [4]: G.add_nodes_from([2, 3]) or add any nbunch of nodes. An nbunch is any iterable container of nodes that is not itself a node in the graph. (e.g. a list, set, graph, file, etc..) In [5]: H = nx.path_graph(10) file:///home/szwabin/Dropbox/Praca/Zajecia/Diffusion/Lectures/1_intro/networkx_tutorial/tutorial.html 1/18 5.03.2020 tutorial In [6]: G.add_nodes_from(H) Note that G now contains the nodes of H as nodes of G. In contrast, you could use the graph H as a node in G. -
Networkx: Network Analysis with Python
NetworkX: Network Analysis with Python Salvatore Scellato Full tutorial presented at the XXX SunBelt Conference “NetworkX introduction: Hacking social networks using the Python programming language” by Aric Hagberg & Drew Conway Outline 1. Introduction to NetworkX 2. Getting started with Python and NetworkX 3. Basic network analysis 4. Writing your own code 5. You are ready for your project! 1. Introduction to NetworkX. Introduction to NetworkX - network analysis Vast amounts of network data are being generated and collected • Sociology: web pages, mobile phones, social networks • Technology: Internet routers, vehicular flows, power grids How can we analyze this networks? Introduction to NetworkX - Python awesomeness Introduction to NetworkX “Python package for the creation, manipulation and study of the structure, dynamics and functions of complex networks.” • Data structures for representing many types of networks, or graphs • Nodes can be any (hashable) Python object, edges can contain arbitrary data • Flexibility ideal for representing networks found in many different fields • Easy to install on multiple platforms • Online up-to-date documentation • First public release in April 2005 Introduction to NetworkX - design requirements • Tool to study the structure and dynamics of social, biological, and infrastructure networks • Ease-of-use and rapid development in a collaborative, multidisciplinary environment • Easy to learn, easy to teach • Open-source tool base that can easily grow in a multidisciplinary environment with non-expert users -
Graphprism: Compact Visualization of Network Structure
GraphPrism: Compact Visualization of Network Structure Sanjay Kairam, Diana MacLean, Manolis Savva, Jeffrey Heer Stanford University Computer Science Department {skairam, malcdi, msavva, jheer}@cs.stanford.edu GraphPrism Connectivity ABSTRACT nodeRadius 7.9 strokeWidth 1.12 charge -242 Visual methods for supporting the characterization, com- gravity 0.65 linkDistance 20 parison, and classification of large networks remain an open Transitivity updateGraph Close Controls challenge. Ideally, such techniques should surface useful 0 node(s) selected. structural features { such as effective diameter, small-world properties, and structural holes { not always apparent from Density either summary statistics or typical network visualizations. In this paper, we present GraphPrism, a technique for visu- Conductance ally summarizing arbitrarily large graphs through combina- tions of `facets', each corresponding to a single node- or edge- specific metric (e.g., transitivity). We describe a generalized Jaccard approach for constructing facets by calculating distributions of graph metrics over increasingly large local neighborhoods and representing these as a stacked multi-scale histogram. MeetMin Evaluation with paper prototypes shows that, with minimal training, static GraphPrism diagrams can aid network anal- ysis experts in performing basic analysis tasks with network Created by Sanjay Kairam. Visualization using D3. data. Finally, we contribute the design of an interactive sys- Figure 1: GraphPrism and node-link diagrams for tem using linked selection between GraphPrism overviews the largest component of a co-authorship graph. and node-link detail views. Using a case study of data from a co-authorship network, we illustrate how GraphPrism fa- compactly summarizing networks of arbitrary size. Graph- cilitates interactive exploration of network data. -
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 -
Graph Simplification and Interaction
Graph Clarity, Simplification, & Interaction http://i.imgur.com/cW19IBR.jpg https://www.reddit.com/r/CableManagement/ Today • Today’s Reading: Lombardi Graphs – Bezier Curves • Today’s Reading: Clustering/Hierarchical edge Bundling – Definition of Betweenness Centrality • Emergency Management Graph Visualization – Sean Kim’s masters project • Reading for Tuesday & Homework 3 • Graph Interaction Brainstorming Exercise "Lombardi drawings of graphs", Duncan, Eppstein, Goodrich, Kobourov, Nollenberg, Graph Drawing 2010 • Circular arcs • Perfect angular resolution (edges for equal angles at vertices) • Arcs only intersect 2 vertices (at endpoints) • (not required to be crossing free) • Vertices may be constrained to lie on circle or concentric circles • People are more patient with aesthetically pleasing graphs (will spend longer studying to learn/draw conclusions) • What about relaxing the circular arc requirement and allowing Bezier arcs? • How does it scale to larger data? • Long curved arcs can be much harder to follow • Circular layout of nodes is often very good! • Would like more pseudocode Cubic Bézier Curve • 4 control points • Curve passes through first & last control point • Curve is tangent at P0 to (P1-P0) and at P3 to (P3-P2) http://www.e-cartouche.ch/content_reg/carto http://www.webreference.com/dla uche/graphics/en/html/Curves_learningObject b/9902/bezier.html 2.html “Force-directed Lombardi-style graph drawing”, Chernobelskiy et al., Graph Drawing 2011. • Relaxation of the Lombardi Graph requirements • “straight-line segments -
Constraint Graph Drawing
Constrained Graph Drawing Dissertation zur Erlangung des akademischen Grades des Doktors der Naturwissenschaften vorgelegt von Barbara Pampel, geb. Schlieper an der Universit¨at Konstanz Mathematisch-Naturwissenschaftliche Sektion Fachbereich Informatik und Informationswissenschaft Tag der mundlichen¨ Prufung:¨ 14. Juli 2011 1. Referent: Prof. Dr. Ulrik Brandes 2. Referent: Prof. Dr. Michael Kaufmann II Teile dieser Arbeit basieren auf Ver¨offentlichungen, die aus der Zusammenar- beit mit anderen Wissenschaftlerinnen und Wissenschaftlern entstanden sind. Zu allen diesen Inhalten wurden wesentliche Beitr¨age geleistet. Kapitel 3 (Bachmaier, Brandes, and Schlieper, 2005; Brandes and Schlieper, 2009) Kapitel 4 (Brandes and Pampel, 2009) Kapitel 6 (Brandes, Cornelsen, Pampel, and Sallaberry, 2010b) Zusammenfassung Netzwerke werden in den unterschiedlichsten Forschungsgebieten zur Repr¨asenta- tion relationaler Daten genutzt. Durch geeignete mathematische Methoden kann man diese Netzwerke als Graphen darstellen. Ein Graph ist ein Gebilde aus Kno- ten und Kanten, welche die Knoten verbinden. Hierbei k¨onnen sowohl die Kan- ten als auch die Knoten weitere Informationen beinhalten. Diese Informationen k¨onnen den einzelnen Elementen zugeordnet sein, sich aber auch aus Anordnung und Verteilung der Elemente ergeben. Mit Algorithmen (strukturierten Reihen von Arbeitsanweisungen) aus dem Gebiet des Graphenzeichnens kann man die unterschiedlichsten Informationen aus verschiedenen Forschungsbereichen visualisieren. Graphische Darstellungen k¨onnen das -
Chapter 18 Spectral Graph Drawing
Chapter 18 Spectral Graph Drawing 18.1 Graph Drawing and Energy Minimization Let G =(V,E)besomeundirectedgraph.Itisoftende- sirable to draw a graph, usually in the plane but possibly in 3D, and it turns out that the graph Laplacian can be used to design surprisingly good methods. n Say V = m.Theideaistoassignapoint⇢(vi)inR to the vertex| | v V ,foreveryv V ,andtodrawaline i 2 i 2 segment between the points ⇢(vi)and⇢(vj). Thus, a graph drawing is a function ⇢: V Rn. ! 821 822 CHAPTER 18. SPECTRAL GRAPH DRAWING We define the matrix of a graph drawing ⇢ (in Rn) as a m n matrix R whose ith row consists of the row vector ⇥ n ⇢(vi)correspondingtothepointrepresentingvi in R . Typically, we want n<m;infactn should be much smaller than m. Arepresentationisbalanced i↵the sum of the entries of every column is zero, that is, 1>R =0. If a representation is not balanced, it can be made bal- anced by a suitable translation. We may also assume that the columns of R are linearly independent, since any basis of the column space also determines the drawing. Thus, from now on, we may assume that n m. 18.1. GRAPH DRAWING AND ENERGY MINIMIZATION 823 Remark: Agraphdrawing⇢: V Rn is not required to be injective, which may result in! degenerate drawings where distinct vertices are drawn as the same point. For this reason, we prefer not to use the terminology graph embedding,whichisoftenusedintheliterature. This is because in di↵erential geometry, an embedding always refers to an injective map. The term graph immersion would be more appropriate. -
Facing the Future: European Research Infrastructures for the Humanities and Social Sciences
Facing the Future: European Research Infrastructures for the Humanities and Social Sciences Adrian Duşa, Dietrich Nelle, Günter Stock, and Gert G. Wagner (Eds.) Facing the Future: European Research Infrastructures for the Humanities and Social Sciences E d i t o r s : Adrian Duşa (SCI-SWG), Dietrich Nelle (BMBF), Günter Stock (ALLEA), and Gert. G. Wagner (RatSWD) ISBN 978-3-944417-03-5 1st edition © 2014 SCIVERO Verlag, Berlin SCIVERO is a trademark of GWI Verwaltungsgesellschaft für Wissenschaftspoli- tik und Infrastrukturentwicklung Berlin UG (haftungsbeschränkt). This book documents the results of the conference Facing the Future: European Research Infrastructure for Humanities and Social Sciences (November 21/22 2013, Berlin), initiated by the Social and Cultural Innovation Strategy Work- ing Group of ESFRI (SCI-SWG) and the German Federal Ministry of Education and Research (BMBF), and hosted by the European Federation of Academies of Sciences and Humanities (ALLEA) and the German Data Forum (RatSWD). Thanks and appreciation are due to all authors, speakers and participants of the conference, and all involved institutions, in particular the German Federal Ministry of Education and Research (BMBF). The ministry funded the conference and this subsequent publication as part of the Union of the German Academies of Sciences and Humanities’ project “Survey and Analysis of Basic Humanities and Social Science Research at the Science Academies Related Research Insti- tutes of Europe”. The views expressed in this publication are exclusively the opinions of the authors and not those of the German Federal Ministry of Education and Research. Editing: Dominik Adrian, Camilla Leathem, Thomas Runge, Simon Wolff Layout and graphic design: Thomas Runge Contents Preface . -
Extracting Insights from Differences: Analyzing Node-Aligned Social
Extracting Insights from Differences: Analyzing Node-aligned Social Graphs by Srayan Datta A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Computer Science and Engineering) in The University of Michigan 2019 Doctoral Committee: Associate Professor Eytan Adar, Chair Associate Professor Mike Cafarella Assistant Professor Danai Koutra Associate Professor Clifford Lampe Srayan Datta [email protected] ORCID iD: 0000-0002-5800-830X c Srayan Datta 2019 To my family and friends ii ACKNOWLEDGEMENTS There are several people who made this dissertation possible, first among this long list is my adviser, Eytan Adar. Pursuing a doctoral program after just finishing un- dergraduate studies can be a daunting task but Eytan made it easy with his patience, kindness, and guidance. I learned a lot from our collaborations and idle conversations and I am very grateful for that. I would like to extend my thanks to the rest of my thesis committee, Mike Ca- farella, Danai Koutra and Cliff Lampe for their suggestions and constructive feed- back. I would also like to thank the following faculty members, Daniel Romero, Ceren Budak, Eric Gilbert and David Jurgens for their long insightful conversations and suggestions about some of my projects. I would like to thank all of friends and colleagues who helped (as a co-author or through critique) or supported me through this process. This is an enormous list but I am especially thankful to Chanda Phelan, Eshwar Chandrasekharan, Sam Carton, Cristina Garbacea, Shiyan Yan, Hari Subramonyam, Bikash Kanungo, and Ram Srivatasa. I would like to thank my parents for their unwavering support and faith in me. -
Network Analysis with Nodexl
Social data: Advanced Methods – Social (Media) Network Analysis with NodeXL A project from the Social Media Research Foundation: http://www.smrfoundation.org About Me Introductions Marc A. Smith Chief Social Scientist Connected Action Consulting Group [email protected] http://www.connectedaction.net http://www.codeplex.com/nodexl http://www.twitter.com/marc_smith http://delicious.com/marc_smith/Paper http://www.flickr.com/photos/marc_smith http://www.facebook.com/marc.smith.sociologist http://www.linkedin.com/in/marcasmith http://www.slideshare.net/Marc_A_Smith http://www.smrfoundation.org http://www.flickr.com/photos/library_of_congress/3295494976/sizes/o/in/photostream/ http://www.flickr.com/photos/amycgx/3119640267/ Collaboration networks are social networks SNA 101 • Node A – “actor” on which relationships act; 1-mode versus 2-mode networks • Edge B – Relationship connecting nodes; can be directional C • Cohesive Sub-Group – Well-connected group; clique; cluster A B D E • Key Metrics – Centrality (group or individual measure) D • Number of direct connections that individuals have with others in the group (usually look at incoming connections only) E • Measure at the individual node or group level – Cohesion (group measure) • Ease with which a network can connect • Aggregate measure of shortest path between each node pair at network level reflects average distance – Density (group measure) • Robustness of the network • Number of connections that exist in the group out of 100% possible – Betweenness (individual measure) F G • -
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. -
Graph Drawing by Stochastic Gradient Descent
1 Graph Drawing by Stochastic Gradient Descent Jonathan X. Zheng, Samraat Pawar, Dan F. M. Goodman Abstract—A popular method of force-directed graph drawing is multidimensional scaling using graph-theoretic distances as input. We present an algorithm to minimize its energy function, known as stress, by using stochastic gradient descent (SGD) to move a single pair of vertices at a time. Our results show that SGD can reach lower stress levels faster and more consistently than majorization, without needing help from a good initialization. We then show how the unique properties of SGD make it easier to produce constrained layouts than previous approaches. We also show how SGD can be directly applied within the sparse stress approximation of Ortmann et al. [1], making the algorithm scalable up to large graphs. Index Terms—Graph drawing, multidimensional scaling, constraints, relaxation, stochastic gradient descent F 1 INTRODUCTION RAPHS are a common data structure, used to describe to the shortest path distance between vertices i and j, with w = d−2 G everything from social networks to food webs, from ij ij to offset the extra weight given to longer paths metabolic pathways to internet traffic. Any set of pairwise due to squaring the difference [3]. relationships between entities can be described by a graph, This definition was popularized for graph layout by and the ever increasing amount of data being collected Kamada and Kawai [4] who minimized the function using means that visualizing graphs for exploratory analysis has a localized 2D Newton-Raphson method, while within the become an important task. MDS community Kruskal [5] originally used gradient de- Node-link diagrams are an intuitive representation of scent [6].