Complex networks considering both positive and negative links have gained considerable attention during the past several years. Community detection is one of the main challenges for complex network analysis. Most of the existing algorithms for community detection in a signed network aim at providing a hard-partition of the network where any node should belong to a community or not. However, the...

One of the most remarkable social phenomena is the formation of communities in social networks corresponding to families, friendship circles, work teams, etc. Since people usually belong to several different communities at the same time, the induced overlaps result in an extremely complicated web of the communities themselves. Thus, uncovering the intricate community structure of social network...

In a very general context, communities in networks are defined as groups of nodes that have some common properties such that connections are stronger between the nodes in a community than with the nodes in the rest of the network. It is quite common for nodes to participate in multiple communities. Therefore a community detection algorithm for such applications should be able to detect overlapp...

Introduction With the growth of social media, social network analysis draws a great attention and becomes a hot research topic in the field of complex network, web mining, information retrieval, etc. An important aspect of social networks analysis is community structure (Newman, 2003). In general, community detection methods are classified into two categories: overlapping methods (and non-overl...

Folksonomies like Delicious and LastFm are modeled as multilateral (user-resource-tag) hypergraphs for studying their network properties. Detecting communities of similar nodes from such networks is a challenging problem. Most existing algorithms for community detection in folksonomies assign unique communities to nodes, whereas in reality, users have multiple relevant interests and same resour...

We present a new online algorithm for detecting overlapping communities. The main ingredients are a modification of an online k-means algorithm and a new approach to modelling overlap in communities. An evaluation on large benchmark graphs shows that the quality of discovered communities compares favourably to several methods in the recent literature, while the running time is significantly imp...

Community detection in complex network has become a vital step to understand the structure and dynamics of networks in various fields. However, traditional node clustering and relatively new proposed link clustering methods have inherent drawbacks to discover overlapping communities. Node clustering is inadequate to capture the pervasive overlaps, while link clustering is often criticized due t...

Due to the increasingly large size and changing nature of social networks, algorithms for dynamic networks have become an important part of modern day community detection. In this paper, we use a well-known static community detection algorithm and modify it to discover communities in dynamic networks. We have developed a dynamic community detection algorithm based on Speaker-Listener Label Prop...

Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive re...

Available online 14 May 2013 There is a surge of community detection study on complex network analysis in recent years, since communities often play important roles in network systems. However, many real networks have more complex overlapping community structures. This paper proposes a novel algorithm to discover overlapping communities. Different from conventional algorithms based on node clus...