IC05 Introduction on Networks &Visualization Nov

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1 IC05 Introduction on Networks &Visualization Nov

2 Overview 1. Networks Introduction Networks across disciplines Properties Models 2. Visualization InfoVis Data exploration and interaction Design Samples 3. Information Systems 4. Conclusion

3 Networks / Introduction

4 Networks / Introduction Where are networks?

5 Networks / Introduction What is a complex system? Complex systems are characterized by global, emergent properties (self organizing) Complicated Complex

6 Networks / Networks across disciplines

7 Networks / Networks across disciplines Internet French Political Blogosphere (2007) RTGI

8 Networks / Networks across disciplines Social networks Global Jihad Terrorist Network

9 Networks / Networks across disciplines Software graphs Codeminer (2008)

10 Networks / Networks across disciplines And more: Biological networks Semantic networks Transportation networks Food webs Bibliography networks Routers Brain cells Hollywood actors Sexual networks They are all around, do they have properties in common?

11 Networks / Properties & Metrics

12 Networks / Properties & Metrics Power law distribution number of nodes with so many edges a few nodes with a very large number of edges Long tail: many nodes with few edges Degree of A is 5 number of edges

13 Networks / Properties & Metrics size of giant component if the largest component encompasses a significant fraction of the graph, it is called the giant component

14 Networks / Properties & Metrics Giant Component video

15 Networks / Properties & Metrics What implications have these properties? Robustness Search Communities Spread of disease Opinion formation Spread of computer viruses Gossip

16 Networks / Properties & Metrics What implications have these properties? Robustness Resistant to random attacks Vulnerable to targeted attacks

17 Networks / Properties & Metrics What implications have these properties? Contagion information rumors viruses

18 Networks / Properties & Metrics What implications have these properties? Communities Communities detection = Classification = Clustering

19 Networks / Models

20 Networks / Models How to explain these properties? How to mathematically define these networks? Try to generate real world network with mathematical model Rules behind the growth and dynamic of networks Predict the future?

21 Networks / Models Duncan Watts and Steven Strogatz (1998) a few random links makes a huge difference my friend s friend is always my friend mostly structured with a few random connections all connections random

22 Networks / Models Barabasi Albert Preferential Attachment (2000) the first model of the web rich get richer phenomenon

23 Networks / Models Current research Jon Kleiberg & al., information cycle Alessandro Vespignani & al., Epidemics H1N1 Spread model Network research needs real dataset. We now have plenty of them (Facebook, Blogs, s, Genes, Mobile phones )

24 Visualization / InfoVis

25 Visualization / InfoVis InfoVis = Information + Visualization Data

26 Visualization / InfoVis Type of data Spatial (1,2,3D) Tabular (Multi dimensional) Network, Tree Text, documents Easy user task Min, max, average, % Exact queries, search Harder Patterns, trends, correlations Changes over time, context Anomalies, data errors Geographical representation Excel can do this Visualization can do this

27 Visualization / InfoVis Jacques Bertin s Semiology of Graphics, 1967 Spatial (1,2,3D)

28 Visualization / InfoVis Samples London Tube Map

29 Visualization / InfoVis Samples Is this InfoVis?

30 Visualization / InfoVis Samples Danny Holten (2006), Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data

31 Visualization / InfoVis Samples Many Eyes

32 Visualization / InfoVis Samples Stream graph

33 Visualization / InfoVis Samples

34 Visualization / Data exploration and interaction

35 Visualization / Data exploration and interaction Data exploration, overview and details Zoomable user interface Interactive, iterative, difficult process Automation difficult Empower the user instead InfoVis vs SciVis InfoVis Abstract Spatialization chosen SciVis Scientific & physically based Spatialization given Data exploration is between

36 Visualization / Design People remember design, design makes everything Personal advice, choose good colors, fonts and so on in your presentation

37 Visualization / Design David McCandless, 2009 Great data visualization tells a story

38 Visualization / Samples

39 Information Systems

40 Information Systems Design is not our job, nor making prototypes Where does Computer scientists come? Systems with huge amount of data and users Urbanization and scalability Fields of study Information retrieval (search text and media) Statistics, data mining Graphics and visualization Example: What could we do with a community detection?

41 Conclusion

42 Conclusion To resume, what kind of competences were shown here, and to what process they belong? Computer Science: acquire and parse data Mathematics, Statistics, & Data Mining: filter and mine Graphic Design: represent and refine Infovis and Human Computer Interaction (HCI): interaction

43 Conclusion The power of networks, what structure means Complexity and systemic behavior Visualization is needed, at the edge between data and users Engineers role Interdisciplinary

44 Questions

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