Social Flocks: Simulating Crowds to Discover the Connection between Spatial-Temporal Movements of People and Social Structure

This is Social Flocks supplementary page for the submission to KAIS.


This work combines the central ideas of two different areas, crowd simulation and social network analysis, to tackle existing problems in both areas from a new perspective. We present a novel spatio-temporal social crowd simulation framework, Social Flocks, to revisit three essential research problems, (a) generation of social networks, (b) community detection in social networks, (c) modeling collective social behaviors in crowd simulation. Our framework produces social networks that satisfy the properties of high clustering coefficient, low average path length, and power-law degree distribution. It can also be exploited as a novel dynamic model for community detection that can not only produce the eventual communities but also display the process of how communities are formed. Finally our framework can be used to produce real-life collective social behaviors over crowds, including community-guided flocking, leader following, and spatio-social information propagation. Social Flocks can serve as visualization of simulated crowds for domain experts to explore the dynamic effects of the spatial, temporal, and social factors on social networks. In addition, it provides an experimental platform of collective social behaviors for social gaming and movie animations.

A. CrowdNetGen: Generating Realistic Social Networks

B. Crowdstering: Network Community Detection

C. SocioCrowd: Modeling Collective Social Behaviors


Latest Update: Dec. 15, 2014.