Currently, there is a growing interest in identifying communities in social networks. Although there are many algorithms that suitably resolve this problem, they do not properly find overlaps among communities. This paper describes a new approach to the detection of overlapping communities based on the ideas of friendship and leadership, using a new centrality measure, called extended degree. W...

Network communities represent mesoscopic structure for understanding the organization of real-world networks, where nodes often belong to multiple communities and form overlapping community structure in the network. Due to non-triviality in finding the exact boundary of such overlapping communities, this problem has become challenging, and therefore huge effort has been devoted to detect overla...

In this paper, we present a method named OCDLS that is based on link-link similarity and SNMF (symmetric nonnegative matrix factorization) to detect overlapping communities. To begin with constructing the link-link similarity matrix, and then using SNMF to partition links, finally computing the overlapping factor, and assigning nodes to the corresponding communities according to the prespecifie...

Many complex systems can be described as networks to comprehend both the structure and the function. Community structure is one of the most important properties of complex networks. Detecting overlapping communities in networks have been more attention in recent years, but the most of approaches to this problem have been applied to the undirected networks. This paper presents a novel approach b...

Community detection in a social network is an emerging issue in the study of network system as it helps to realize the overall network structure in depth. Communities are the natural partition of network nodes into subgroups where nodes within the subgroup are densely connected but between the subgroups connections are sparser. Real world networks, including social networks have been found to p...

Overlapping community detection is progressively becoming an important issue in complex networks. Many in-memory overlapping community detection algorithms have been proposed for graphs with thousands of nodes. However, analyzing massive graphs with millions of nodes is impossible for the traditional algorithm. In this paper, we propose MR-ECOCD, a novel distributed computation algorithm using ...