Community detection is one of the most important problems in complex networks. Many algorithms have been proposed in …

Recent years have witnessed a growing recognition on the community detection in networks. Diverse techniques have been … community detection problem. In this paper, a novel view to look deep into the network structure from the community level is … tested and a heuristic density-based approach for community detection is put forward. In the proposed method, firstly, both …

With the widespread social networks on the Internet, community detection in social graphs has recently become an … Human Brain Tractography data. This last application leads to the possibility of applying community detection methods to … have encompassed all three types of graphs simultaneously. In this paper, we present a method that unifies community …

algorithms for community detection are being actively developed and the achievements in this area can be utilized for analysis of …

structure is a common and important property. Community detection in complex networks has attracted a lot of attention in recent … years. Community detection is the problem of finding closely related groups within a network. Modularity optimisation is a … widely accepted method for community detection. It has been shown that the modularity optimisation has a resolution limit …

In recent studies of the complex network, most of the community detection methods only consider the network topological …-supervised community detection algorithm is proposed based on the discrete potential theory. It effectively incorporates individual labels …, the labels of corresponding communities, to guide the community detection process for achieving better accuracy …