Community detection and overlapping community detection has been of significant importance in the last decade wherein invention and growth of social networks like Facebook, Twitter, Linkedin, Flickr, etc. has even made it more crucial to investigate better approaches. Overlapping community detection has many application as in modern market analysis and recommendation systems, bioinformatic syst...

With greater availability of data and increasing interaction activities taking place on social media, detecting overlapping and hierarchical communities has become an important issue and one that is essential to social media analysis. In this paper, we propose a coalition formation game theory-based approach to identify overlapping and hierarchical communities. We model community detection as a...

The investigation of network structure has important significance to understand the functions of various complex networks. The communities with hierarchical and overlapping structures and the special nodes like hubs and outliers are all common structure features to the networks. Network structure investigation has attracted considerable research effort recently. However, existing studies have o...

Community structure is a universal and significant feature of many complex networks in biology, society, and economics. Community structure has also been revealed in human brain structural and functional networks in previous studies. However, communities overlap and share many edges and nodes. Uncovering the overlapping community structure of complex networks remains largely unknown in human br...

Overlapping communities in complex network is a common phenomenon in real world network. The overlapping community structure can more accurately obtain the actual structure information in the network. But at present the study of overlapping community division algorithm is relatively less, facing the problems of the low accurate rate. Based on this, this paper presents algorithms OCNS for detect...

Recently, community detection in complex networks has attracted more and more attentions. However, real networks usually have number of overlapping communities. Many overlapping community detection algorithms have been developed. These methods usually consider the overlapping community detection as a single-objective optimization problem. This paper regards it as a multi-objective optimization ...

The detection of community structure is a widely recognised method of revealing the underlying properties of complex networks in biological, physical and social sciences. The simplest form of the community structure problem is the partitioning of unweighted and undirected networks into disjoint communities. However, module detection in weighted networks or communities with overlapping modules m...

We propose an algorithm for finding overlapping community structure in very large networks. The algorithm is based on the label propagation technique of Raghavan, Albert, and Kumara, but is able to detect communities that overlap. Like the original algorithm, vertices have labels that propagate between neighbouring vertices so that members of a community reach a consensus on their community mem...

Real-world networks exhibit significant community structure. Communities are sometimes explicitly known or defined (e.g., virtual groups that one joins in an online social network, departments in an organization), but are often determined using a community detection or a clustering algorithm. Given a weighted network, with edge weights denoting interaction strengths between vertices, and a mapp...

Abstract. Community detection is of great importance for understanding graph structure in social networks. The communities in real-world networks are often overlapped, i.e. some nodes may be a member of multiple clusters. How to uncover the overlapping communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, a novel algorithm to identify...