A Homophily-Free Community Detection Framework for Trajectories with Delayed Responses

Abstract

Community detection has been widely studied in the areas of social network analysis and recommendation system. However, most existing research focus on cases where relationships are explicit or depend on simultaneous appearance. In this paper, we propose to study the community detection problem where the relationships are not based on simultaneous appearance, but time-delayed appearances. In other words, we aim to capture the relationship where one individual physically follows another individual. In our attempt to capture such relationships, the major challenge is the presence of spatial homophily, i.e., individuals are attracted to locations due to their popularities and not because of communications. In tackling the community detection problem with spatial homophily and delayed responses, we make the following key contributions: (1) We introduce a four-phase framework, which by way of using quantified impacts excludes homophily. (2) To validate the framework, we generate a synthetic dataset based on a known community structure and then infer that community structure. (3) Finally, we execute this framework on a real-world dataset with more than 6,000 taxis in Singapore. Our results are also compared to those of a baseline approach without homophily-elimination.