We Know Who You Are: Discovering Similar Groups Across Multiple Social Networks

People use various online social networks for different purposes. The user information on each social network is usually partial. Thus, matching the users across these multiple online social networks is of great significance for providing new services as well as new insights on user behaviors. Recent research shows that group structure widely exists on social networks, in which members work together with certain purpose and are more influential than individuals on online social networks. Previous works provide outstanding solutions for mapping individuals, but few ones pay attention to the study of groups across multiple social networks. To address the research gap, we first aim to propose an effective method to detect similar group across multiple social networks. The method mainly has three steps, including detecting group structure based on random walks, extracting similarity features, and inferring group similarity using probabilistic graphical model. We evaluate our algorithm on five different types of online social networks. Experimental results show that our method achieves 0.693, 0.779, 0.729 in precision, recall, and F1-measure, which significantly surpass the state-of-the-art by 31.4%-44.3%, 17.3%-26.3%, and 25.9%-31.6%, respectively. The outstanding performance of our method demonstrates that our proposal can reach the requirement of detecting similar groups across social networks. In particular, the result of this paper paves the way for the recommendation system, link prediction and information diffusion across sites.