Visualizing the Evolution of Community Structures in Dynamic Social Networks

1. Visualizing the Evolution of Community Structures in Dynamic Social Networks Khairi Reda Department of Computer Science University of Illinois at Chicago

2. Social Networks Analysis (SNA) • SNA is concerned with structures of ties in the social system, rather than behavior of individual actors • Visualization has been a central theme in SNA since its inception • Graphs are the most common visual representation • Efficient graph layouts make structural patterns emerge while reducing clutter • Provide a static snapshot of the network - but social systems are dynamic J. Moreno. Who Shall Survive, 1953

3. Dynamic networks T2 T1 Actual • Time related questions • How do diseases/information spread through population? • How do social structures (communities) change with outside circumstances? • What is the lifespan of a social structure, and are there recurring structures? Static Grevy’s zebras communities

4. Dynamic Networks Vis Animated graphs • Limitations • Node movement should be minimized to maintain “mental map” => Potentially poor local layouts • Limited short-term visual memory • Can see momentarily changes, but not long-term patterns • Scalability hampered

5. Dynamic Networks Vis Stacked graphs T3 T2 T1 • Limitations • Substantial redundancy => limited scalability • Layout not necessarily stable across time slices • Suffers form 3D artifacts Corman et al., 2003 Groh et al. Dyson, 2009

6. Design goals • Social scientists need to understand how the social structure evolves and reacts to external circumstances • Communities (social groups) are among the most important phenomena • A group of actors interacting closely and frequently • Fluid membership: individuals switch community affiliation over time • Dynamic community = dynamic clusters • Community = identity • Vis need to show evolution of communities along with domain variables to enable cause-effect analysis

7. Community identification • Given an interaction sequence, assign a community color to each individual at every timestep. • Assumptions • Individual are reluctant to switch community afffiliation - switching cost • Individual mostly are seen with their own community - visiting cost • Individuals are rarely absent from their own community - absence cost • Minimize the total cost across all individuals over interaction sequence • Temporal resolution maintained at its finest-grain

8. Community identification • Given an interaction sequence, assign a community color to each individual at every timestep. • Assumptions • Individual are reluctant to switch community afffiliation - switching cost • Individual mostly are seen with their own community - visiting cost • Individuals are rarely absent from their own community - absence cost • Minimize the total cost across all individuals over interaction sequence • Temporal resolution maintained at its finest-grain

9. Visual metaphor Movie narrative charts, http://xkcd.com/657/ N. Wook Kim et al. TimeNets, AVI ’10

10. Visual metaphor Q R X Y Community affiliation switch A B C

11. Case study: Visualizing communities in the US House of Representatives • 500 roll-call votes between Jan 13 to July 30, 2010 • 434 legislators • Each vote considered to occur in a separate timestep (500 timesteps) • Individuals casting the same vote (Aye, Nay, or Not Voting) considered to be interacting with each other at that time • Communities = political opinion groups

12. Case study: Visualizing communities in the US House of Representatives

13. Case study: Visualizing communities in the US House of Representatives Actual vote on Kucinich’s resolution March 15 - vote to consider debating Kucinich’s resolution for Afghanistan troops withdrawal by 2010

14. Case study: Visualizing communities in the US House of Representatives Republicans and conservative democrats - opposing discussion of proposal Centrist democrats - opposing discussion of proposal Main stream democrats - supporting discussion of proposal Liberal democrats - supporting withdrawal Actual vote on Kucinich’s resolution March 15 - vote to consider debating Kucinich’s resolution for Afghanistan troops withdrawal by 2010

15. User study • Behavioral ecologists want to understand how ecological factors (resources, predation risk, etc.) influence the social structure of group-living populations • Grevy’s zebras • Endangered population of about 3,000 • Fission-fusion social structure • 35 individuals observed over a period of 3 months in 2003 in Laikipia, Kenya • Social interactions inferred from physical proximity • Four ecology researchers analyzed their Grevy’s zebra dataset using our visualization. • Session was video and audio taped followed by a short interview

16. User study Community movement in space Community timeline Individual Purple Community Orange Community

17. User study • Community timeline was intuitive to domain scientists • “This is a very clean depiction of community membership. It is easier to see the individuals move [between communities]” • Supports correlation of attributes with structural changes in the network • “We are looking at a different project that shows the individual by [reproductive] state moving in and out of the community” • “This says what the males, lactating, and non-lactating females are doing. It is very powerful analysis to see when the switch happens” Stallion Bachelor Lactating Female Non-Lactating Female

18. User study • Layout stability • “Once we know this is a community, to see the individuals aligned very consistently like this in almost what looks like a British subway map with simple angles is very useful” • Integration of community timeline and movement data • “[This visualization] finally put time and space-together. This allows us to understand the physical decision making that lead to the shaping of communities. The dynamic community analysis gave us a better picture for understanding zebra dynamics. The space will give us even a better picture of that temporality”

19. Limitations • Scalability • Visualization scales well with number of individuals and timesteps, but less so with number of communities • US congress ~ 400 individuals, ~8 communities • Zebra dataset ~35 individuals, ~12 communities • Layout optimization • Minimization of thread crossings, locally and globally

20. Conclusions • Social network visualization needs to catch up and propose solutions for dynamic social networks • Graph layouts have limitations when applied to dynamic networks • The community structure timeline provides an alternative to stacked graphs • Shows coherent, fine-grained view of the evolving community structure • Integration of domain data allow cause-effect analysis

21. Thank you • Khairi Reda - mreda2@uic.edu Electronic Visualization Laboratory University of Illinois at Chicago Computational Population Biology Laboratory University of Illinois at Chicago Funded in part by the National Science Foundation grants CNS-0821121 and OCI-0943559

22. Dynamic Networks Vis Other variants • Ogawa et al. clusters every timestep independently, yet Ogawa et al. APVIS, 2007