We introduce a game-theoretic framework to address the community detection problem based on the social networks’ structure. The dynamics of community formation is framed as a strategic game called community formation game: Given a social network, each node is selfish and selects communities to join or leave based on her own utility measurement. A community structure can be interpreted as an equ...

It is well-known that community detection methods based on modularity optimization often fails to discover small communities. Several objective functions used for community detection therefore involve a resolution parameter that allows the detection of communities at different scales. We provide an explicit upper bound on the community size of communities resulting from the optimization of seve...

Community discovery in social networks has received a significant amount of attention in the social media research community. The techniques developed by the community have become quite adept at identifying the large communities in a network, but often neglect smaller communities. Evaluation techniques also show this bias, as the resolution limit problem in modularity indicates. Small communiti...

The community detection was performed from the perspective of links, and we proposed an inhibition method against social network worms. Firstly, a community detection algorithm was proposed, which based on link clustering, and we got related link incremental information through the network structure information at various time points. In order to obtain the link communities, we adopted an impro...

In this paper, a simple but efficient method of overlapping community detection is presented using local community gravitation in social networks. Given a highquality, non-overlapping partition generated by existing methods, this proposed method identifies the overlapping nodes from their surrounding partitioned communities according to their local community gravitation, with low computational ...

Most current community detection methods for complex networks focus on partition. Community number estimation does not have the due attention it deserves, and the community number is only a by-product of community partition. In fact, knowing the community number in advance can speed up the partition process, especially for large scale and dynamic complex networks. This paper proposes a communit...

Community structure detection in complex networks attracts considerable attention in recent years. In this paper we propose an algorithm to detect community structures in very large networks. Basing on the local community detection, this algorithm is able to detect global community structures. We found that the local maximal degree nodes locate dispersedly in networks and can be considered as k...

We introduce a new conception of community structure, which we refer to as hidden community structure. Hidden community structure refers to a specific type of overlapping community structure, in which the detection of weak, but meaningful, communities is hindered by the presence of stronger communities. We present Hidden Community Detection (HICODE), an algorithm template that identifies both t...