This dissertation is devoted to networks: complex interconnected systems where the individual components are connected by binary links arranged in seemingly random but intrinsically structured patterns. Networks are used to model various real-world phenomena ranging from protein interaction in living organisms to the large-scale organisation of human society or the structure of technological ne...

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...

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...

Community structure is an important feature of complex networks and social network communities refer to groups of individuals within which social interactions are intense and between which they are weak. Community detection algorithms answer a wide range of questions regarding the behavior and interaction patterns of people. Usually, communities in social network can overlap with each other. De...

This paper presents a comprehensive study about different methods which were used to detect the communities in networks. Most of the algorithms were failed to perform in all kind of networks i.e. some perform only in weighted networks, some in bi-partite networks and some in directed networks etc. Label propagation algorithm plays the most effective role in community detection as it takes much ...

Abstract. Recent years have seen the development of many graph clustering algorithms, which can identify community structure in networks. The vast majority of these only find disjoint communities, but in many real-world networks communities overlap to some extent. We present a new algorithm for discovering overlapping communities in networks, by extending Girvan and Newman’s well-known algorith...