Abstract

The real social network and associated communities are often hidden under the declared friend or group lists in social networks. We usually observe the manifestation of these hidden networks and communities in the form of recurrent and time-stamped individuals ’ activities in the social network. Inferring the underlying network and finding coherent communities are therefore two key challenges in social networks analysis. In this paper, we address the following question: Could we simultaneously detect community structure and network infectivity among individuals from their ac-tivities? Based on the fact that the two characteristics intertwine and that knowing one will help better reveal-ing the other, we propose a multidimensional Hawkes process that can address them simultaneously. To this end, we parametrize the network infectivity in terms of individuals ’ participation in communities and the pop-ularity of each individual. We show that this modeling approach has many benefits, both conceptually and ex-perimentally. We utilize Bayesian variational inference to design NetCodec, an efficient inference algorithm which is verified with both synthetic and real world data sets. The experiments show that NetCodec can discover the underlying network infectivity and commu-nity structure more accurately than baseline method. 1