Dynamic Local Community Detection Algorithms

Abstract

Recent years have witnessed the rapid growth of social network services. Real-world social networks are huge and changing over time. Consequently, the problems in this area have become more complex. Community detection is one of the most important problems in social networks. A good community can be defined as a group of nodes that are highly connected to each other and loosely connected to the nodes outside the community. Regarding the fact that social networks are huge in size, having complete information of the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. The problem of community detection in dynamic networks is well-investigated however, the local community detection is not widely addressed by researchers. In this paper, this problem is investigated by employing a number of existing local community detection algorithms in a dynamic structure. Results are reported in two different experiments. Experimental results show that one of the algorithms (algorithm P) outperforms other algorithms regarding the employed dynamic structure. The results indicate that algorithm P is much faster than other local community detection algorithms in dynamic networks.