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Lesson 10 Visualising and Analysing Network Data ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data Lesson 10 Visualising and Analysing Network Data Mentor: Dr. Kam Tin Seong Associate Professor of Information Systems (Practice) School of Information Systems, Singapore Management University Content • Introduction to Graph Visual Analytics • Graph Visualisation in Actions • Basic Principles of Graph – Network data sets – Graph data format • Network Visualisation and Analysis – Network visualisation and analysis process model – Graph layouts and visual attributes – Network metrics • Network visualisation and analysis tools – NodeXL – Gephi 2 10-1 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data 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 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data 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 • Graph visualisation is used to understand online social network - Sociogram 6 10-3 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data Graph Visualisation in action 2 • 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/ 7 Graph Visualisation in action 3 • Using graph visualisation to understand business networks 8 10-4 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data Graph Visualisation in action 4 • Graph visualisation is used to reveal voting patterns among United States senators 9 Graph Visualisation in action 5 • The social graph above shows the email flows amongst a large project team. 10 10-5 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data 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 11 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 12 10-6 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data Graph Visualisation in actin 8 • 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. Graph Visualisation in actin 9 • Community structure in protein-protein interaction networks. Source: http://arxiv.org/pdf/0906.0612.pdf 14 10-7 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data Graph Visualisation in action 9 • Spatial Network Analysis for Multi-modal Urban Transport Systems Source: http://www.snamuts.com/melbourne.html 15 Graph Visualisation in action 10 • Global Maritime Transport http://onlinelibrary.wiley.com/doi/10.1111/j.1471-0374.2011.00355.x/pdf 16 10-8 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data To learn more, go to Visual Complexity Source: http://www.visualcomplexity.com/vc/index.cfm?domain=Transportation%20Networks 17 Source of network data I • Unstructured data (for example, Tweets) 18 10-9 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data Source of network data II • Node and link dataset (for example, phone call) 19 Source of network data III • Transaction records (for example, EZLink card) 20 10-10 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data Source of network data • Company directory such as OneSource and opencorporates (https://opencorporates.com/) 21 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. 22 10-11 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data 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. 23 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. 24 10-12 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data Graph database - Reference: http://neo4j.com/developer/get-started/ 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. 26 10-13 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data Complete Graph • A complete graph is a simple undirected graph in which every pair of distinct vertices is connected by a unique edge. 27 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 28 10-14 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data Basic Visual Attributes 29 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. 30 10-15 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data 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. 31 A weighted graph • Edges with different thickness are used to represent the monthly calls by staffs. 32 10-16 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data An ego-centric graph • Network consisting of an individual and their immediate peers (Heer & Boyd, 2005) 33 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). 34 10-17 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data 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. 35 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. 36 10-18 ISSS608 Visual Analytics and Applications 11/18/2015 Lesson 10: Visualising and Analysing Network Data Measures of Power and Influence - Network Metrics • A collection of statistical measures to report: – the connectivity of a node within a network, – the complexity of a network, – the clusters or sub- groups within a network. 37 Network Metrics: Degree • Degree, the number of
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