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

• Published in: IEEE transactions on computational social systems Vol. 5; no. 1; pp. 33 - 45
• Main Authors: Li, Cheng-Te, Lin, Shou-De
• Format: Journal Article
• Language: English
• Published: IEEE 01.03.2018
• Subjects: Analytical models; Community discovery; Computational modeling; Correlation; crowd simulation; network generation; Social groups; social network; Social network services; spatio-temporal modeling; Trajectory
• ISSN: 2329-924X; 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2017.2763973

Scientific studies from anthropologists, biologists, and sociologists hypothesized people who live in the geo neighborhood have more chances to contact with each other and construct the social relationships. This paper exploits a simulation-based approach to verify such hypothesis through unveiling the connection between the spatial-temporal movements of people and their social relationships. Based on the crowd simulation technique, we design an agent-based framework, social flocks, to model the geo spatial correlation of social elements. We simulate the movements of people to tackle two tasks, social network generation and network community detection. By mapping nodes in the network into agents in the simulation, we examine whether the social networks generated by our model can satisfy the network properties, such as high clustering coefficient, low average path length, and power-law degree distribution. Besides, given a social network, we simulate the social moving behaviors of agents/nodes to study the formation of communities. Experiments conducted for such two tasks verify the proposed hypotheses. Social flocks can also serve as a visualization platform for experts to explore the effects over the spatial, temporal, and social contexts. Through demonstrating how the simulation models are exploited to address social network problems, this paper encourages more studies on this direction.