Mobility and Community Detection Based on Topics of Interest

Human mobility datasets have been used to characterize mobility and social aspects. These datasets range from cellular operator logs to tracking apps in scenarios such as university campuses, vehicles, and conferences. In this paper, we present and characterize the mobility dataset of participants of an academic conference, gathered through a gamification system. To achieve this goal, we mapped the social network formed by attendees in each technical session of the conference into a temporal graph. Furthermore, we discuss a community detection scheme based on topics of interest and analyze the performance of device-to-device (D2D) opportunistic forwarding algorithms. Results show that, although each participant has a high number of contacts, contact time is low.