Hybrid EGU-based group event participation prediction in event-based social networks

The increased popularity of event-based social networks (EBSNs) connects online social communities with offline event activities, and makes it interesting and necessary to understand users' event participation behaviors on this new type of online social platform, especially when groups are explicitly defined as event organizers and have declared memberships from individual users. Accurate event participation prediction can help guide more effective event participation, event organization, and community development. In this work, using data collected from the popular Meetup.com EBSN spanning three major cities with diverse cultures, we first conduct detailed analysis on group- and user-specified event behaviors. Based on this analysis, we propose a group-based event participation prediction framework that uses personalized random walk with restart on a hybrid EGU (event-group category-user) network to capture intrinsic social relationships, and fuses that with newly designed content and contextual features to predict event participation by group members. Detailed evaluations using the Meetup datasets demonstrate that our prediction frameworks achieves high prediction performance with the proposed features.