Unifying Virtual and Physical Worlds: Learning Toward Local and Global Consistency

Abstract

Event-based social networking services, such as Meetup, are capable of linking online virtual interactions to offline physical activities. Compared to mono online social networking services (e.g., Twitter and Google+), such dual networks provide a complete picture of users' online and offline behaviors that more often than not are compatible and complementary. In the light of this, we argue that joint learning over dual networks offers us a better way to comprehensively understand user behaviors and their underlying organizational principles. Despite its value, few efforts have been dedicated to jointly considering the following factors within a unified model: (1) local user contextualization, (2) global structure coherence, and (3) effectiveness evaluation. Toward this end, we propose a novel dual clustering model for community detection over dual networks to jointly model local consistency for a specific user and global consistency of partitioning results across networks. We theoretically derived its solution. In addition, we verified our model regarding multiple metrics from different aspects and applied it to the application of event attendance prediction. © 2017 ACM.