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Lesson 10: Graph Visualisation and Analysis IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Lesson 10 Graph Visualisation and Analysis: Methods and Applications Mentor: Dr. Kam Tin Seong Associate Professor of Information Systems (Practice) School of Information Systems, Singapore Management University Content • Motivation of graph visualisation • Graph Visualisation in Actions • Typology of network data • Graph data file formats • Graph Layout Techniques • Network visualisation and analysis process model • Network Metrics • Network visualisation and analysis tools – Gephi – NodeXL 10-1 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Network in real World • Physical – Transportation (i.e. road, port, rail, etc) – Utility (electricity, water, gas, network cable, etc) – Natural (river, etc) • Abstract – Social media (i.e. e-mail, Facebook, Twitter, Wikipedia, etc) – Organisation (i.e. NGO, politics, customer-company, staff-to-staff, criminal, terrorist, disease, etc) 3 Classical Graph Theory • The Seven Bridges of Königsberg is a historically notable problem in mathematics. Its negative resolution by Leonhard Euler in 1735 laid the foundations of graph theory and prefigured the idea of topology. Source: http://en.wikipedia.org/wiki/Seven_Bridges_of_K%C3%B6nigsberg 4 10-2 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Classical Graph Visualisation and Analysis • Using sociogram, also know as network graph Early 20th-century social graph used in studying workflows among employees Source: Valdis Krebs (2010) “Your Choice Reveal Who You Are: Mining and Visualizing Social Patterns” in Beautiful Visualization. 5 Graph Visualisation in action 1 • Using graph visualisation to understand business networks 6 10-3 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Graph Visualisation in action 2 • Graph visualisation is used to reveal voting patterns among United States senators Graph Visualisation in action 3 • Graph visualisation is used to understand online social network 8 10-4 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Graph Visualisation in action 4 • Graph visualisation is used to show how the news are all connected by degrees of separation Link: http://whichlight.github.io/reddit-network- vis/?discussion=http://reddit.com/r/gaming/comments/3d2ewz/ninten do_president_satoru_iwata_passes_away/ 9 Graph Visualisation in actin 5 • SNA in Construction Industry Source: Pryke, S.D.”Analysing construction project coalitions: exploring the application of social network analysis”, Construction Management and Economics, (2004), 22. pp. 787-797. 10-5 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Graph Visualisation in action 6 • (a) Similarity graph of log entries and (b) Similarity graph of network scans (b) (a) Source: Graph Drawing for Security Visualization Graph Visualisation in action 7 • Networks of the below universities are expanded in a breadth first manner up to the depth of 2, (showing university, alumni and companies they are associated with through employment, investment or other activities) • Size of the node reflects degree of the node (scaled logarithmically). Source: http://www.innovation-ecosystems.org/wp-content/uploads/2010/12/2011.educon.pdf 10-6 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Graph Visualisation in action 8 • Degree centrality indexes for nodes in the existing (2006) and proposed (2020) public transport networks in Melbourne’s north-east Graph Visualisation in action 9 • Maritime degree, centrality and vulnerability: port hierarchies and emerging areas in containerized transport (2008–2010) 10-7 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Graph visualization in action 10 • S&T cooperation network diagram of cities in China To learn more, go to Visual Complexity Source: http://www.visualcomplexity.com/vc/index.cfm?domain=Transportation%20Networks 16 10-8 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis What are interaction data? • A table showing phone call links between 10 staff in a company. Potential source of network data • Transaction records (for example, EZLink card) 18 10-9 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Potential source of network data • Link records (for example, board of directors) 19 Potential source of network data • Unstructured data (for example, Tweets) 20 10-10 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis An undirected link table • Focus on interaction but not the direction. – For example C1 -> C6 and C6 -> C1 are considered as one interaction. • The table is symmetric. A directed link table • Focus on both interaction and direction. – For example C1 -> C6 and C6 -> C1 are considered as two different interactions. • The table is asymmetric. 10-11 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Graph Data File Formats • GraphML, an XML-based file format for graphs. • GXL, graph exchange format based on XML. • Trivial Graph Format, simple text based format. • GML is another widely used graph exchange format. • DGML, Directed Graph Markup Language from Microsoft. • XGMML, an XML-based graph markup language closely related to GML. • Dot Language, a format for describing graphs and their presentation, for the Graphviz set of tools. 23 Graph database - Reference: http://neo4j.com/developer/get-started/ 10-12 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Graph representation • On the left is a normal graph, in the centre is a graph in which each edge is given a numerical value, and to the right is a directed graph. Complete Graph • A complete graph is a simple undirected graph in which every pair of distinct vertices is connected by a unique edge. 10-13 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis A sample graph • A complete graph is a simple undirected graph in which every pair of distinct vertices (also known as nodes) are connected by an unique edge. edge vertex A directed graph • Have a clear origin and destination. • Also known as asymmetric edges. • Suitable for representing network with non reciprocal relationships such as Twitter. 10-14 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis A weighted graph • A weighted edge includes values associated with each edge that indicate the strength or frequency of tie. – For example, numbers of calls between two staffs. A weighted graph • Edges with different thickness are used to represent the monthly calls by staffs. 10-15 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis An ego-centric graph • Network consisting of an individual and their immediate peers (Heer & Boyd, 2005) Bipartite Graph • A graph whose vertices can be divided into two disjoint sets U and V such that every edge connects a vertex in U to one in V; that is, U and V are independent sets. • Equivalently, a bipartite graph is a graph that does not contain any odd- length cycles. 32 10-16 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Affiliation Networks - Bipartite Graph • Nodes are partitions into two subsets and all Two-mode view of the Southern lines are between pairs of nodes belonging to Women social event dataset different subsets • As there are g actors and h events, there are g + h nodes • The lines on the graph represent the relation “is affiliated with” from the perspective of the actor and the relation “has as a member” from the perspective of the event. • No two actors are adjacent and no two events are adjacent. If pairs of actors are reachable, it is only via paths containing one or more events. Similarly, if pairs of events are reachable, it is only via paths containing one or more actors. Valdis Krebs (2010) “Your Choice Reveal Who You Are: Mining and Visualizing Social Patterns” in Beautiful Visualization. A Multimodel Graph • Social network connecting different types of vertices. – For example, a network may connect peers to discussion forums and blog posts they have commented on. SeriousEats visualization emphasizing the most important people (black circles), forums (orange squares), and blogs (blue diamonds). 10-17 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Adjacency Matrix 35 Graph Layouts 36 10-18 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Conventional graph drawing • Showing node-edge relationship. • Very challenging for large graph. Issues to consider when representing structured data through a graph • Positioning of nodes • Representation of the edges • Dimensioning • Interaction with graphs 10-19 IS428 Visual Analytics for Business Intelligence 10/29/2015 Lesson 10: Graph Visualisation and Analysis Graph Layout Techniques • Force-directed • Radial • Tree Source: http://en.wikipedia.org/wiki/Graph_drawing Force-Directed
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