Detecting activity-based communities using dynamic membership propagation

Existing literature on network community detection typically exploits the structure of static associations between entities. However, real world network data often consists of observations of coordinated interactions between members who belong to multiple communities. This paper presents a novel perspective and approach for activity-based community detection, where a community is defined as a group of actors engaged in correlated activities over time. Detection is performed by propagating membership iteratively to neighboring nodes through edges that represent interactions. We compare the proposed approach to two state-of-the-art methods based on modularity, and demonstrate its effectiveness on a simulated vehicle movement dataset and the Enron email corpus.