Community detection using diffusion information

Community detection in social networks has become a popular topic of research during the last decade. There exist a variety of algorithms for modularizing the network graph into different communities. However, they mostly assume that partial or complete information of the network graphs are available which is not feasible in many cases. In this paper, we focus on detecting communities by exploiting their diffusion information. To this end, we utilize the Conditional Random Fields (CRF) to discover the community structures. The proposed method (CoDi) does not require any prior knowledge about the network structure or specific properties of communities. Furthermore, in contrast to the structure based community detection methods, this method is able to identify the hidden communities. The experimental results indicate considerable improvements in detecting communities based on accuracy, scalability and real cascade information measures.