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

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

Dynamic networks Static Grevy’s zebras communities T1T1 T2T2 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? Actual

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 Dynamic Networks Vis Animated graphs

Limitations Substantial redundancy => limited scalability Layout not necessarily stable across time slices Suffers form 3D artifacts T1T1 T2T2 T3T3 Groh et al. Dyson, 2009 Corman et al., 2003 Dynamic Networks Vis Stacked graphs

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

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

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

Movie narrative charts, Visual metaphor N. Wook Kim et al. TimeNets, AVI ’10

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

500 roll-call votes between Jan 13 to July 30, 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 Case study: Visualizing communities in the US House of Representatives

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

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

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

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

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 Lactating Female Non-Lactating Female Bachelor

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”

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

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

Thank you Khairi Reda - 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 and OCI

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