Biological functions are carried out by groups of interacting molecules, cells or tissues, known as communities. Membership in these communities may overlap when biological components are involved in multiple functions. However, traditional clustering methods detect non-overlapping communities. These detected communities may also be unstable and difficult to replicate, because traditional metho...

We propose a framework to unfold the ego-centered community structure of a given node in a network. The framework is not based on the optimization of a quality function, but on the study of the irregularity of the decrease of a similarity measure. It is a practical use of the notion of multi-ego-centered community and we validate the pertinence of the approach on a real-world network of wikiped...

In this paper, a simple but efficient method of overlapping community detection is presented using local community gravitation in social networks. Given a highquality, non-overlapping partition generated by existing methods, this proposed method identifies the overlapping nodes from their surrounding partitioned communities according to their local community gravitation, with low computational ...

A hierarchical overlapping community discovery algorithm based on node purity (OCFN-PN) is proposed in the paper. This algorithm chooses the maximal relative centrality as the initial community, which solves the problem of inconsistent results of the community discovery algorithm based on fitness resulting from randomly choosing nodes. Before optimizing and merging communities, the community ov...

Identification of (overlapping) communities/clusters in a complex network is a general problem in data mining of network data sets. In this paper, we devise a novel algorithm to identify overlapping communities in complex networks by the combination of a new modularity function based on generalizing NG’s Q function, an approximation mapping of network nodes into Euclidean space and fuzzy c-mean...

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Community detection in a Social network is a trending issue in the study of network system and it helps to realize and study the overall network structure in detail. Communities are basically the partition of network nodes into some subgroups in wh...

Complex networks is an interdisciplinary research area getting attention from a variety of disciplines including sociology, biology, and computer science. It studies the properties of complex systems that may have many functional or structural subunits. Community detection algorithms are one of the major approaches to analyse complex networks by finding these intermediate-level subunits called ...

The advent of social networks poses severe threats on user privacy as adversaries can de-anonymize users’ identities by mapping them to correlated cross-domain networks. Without ground-truth mapping, prior literature proposes various cost functions in hope of measuring the quality of mappings. However, there is generally a lacking of rationale behind the cost functions, whose minimizer also rem...