Discovering Topic-Oriented Highly Interactive Online Communities

Abstract

Community detection is an interesting field of online social network. Most existing approaches either consider common attributes of social network users or rely on only social connections among the users. However, not enough attention is paid to the degree of interactions among the community members in the retrieved communities, resulting in less interactive community members. This inactivity will create problem for many business companies as they require highly interactive users to efficiently advertise their marketing information. In this paper, we propose a model to detect topic oriented densely-connected communities in which community members have active interactions among them. We conduct experiments on real dataset to demonstrate the effectiveness of our proposed approach.