Discovering overlapping community structures is a crucial step to understanding the structure and dynamics of many networks. In this paper we develop a symmetric binary matrix factorization model to identify overlapping communities. Our model allows us not only to assign community memberships explicitly to nodes, but also to distinguish outliers from overlapping nodes. In addition, we propose a...

Community structure can be observed in many natural, biological and social networks. Studies suggest that these communities may have organized in a hierarchical manner while some communities overlap with others. This paper introduces an algorithm to detect such hierarchical and overlapping community structures in networks based on the concept of maximal cliques. It introduces an alternate modul...

This article is about community detection algorithms in graphs. First a new method will be introduced, which is based on an extension [16] of the commonly used modularity [17, 18, 19, 20] and gives overlapping communities. We list and compare the results given by our new method and some other algorithms yileding either overlapping or non-overlapping communities. While the main use of the propos...

We introduce a game-theoretic framework to address the community detection problem based on the social networks’ structure. The dynamics of community formation is framed as a strategic game called community formation game: Given a social network, each node is selfish and selects communities to join or leave based on her own utility measurement. A community structure can be interpreted as an equ...

Large-scale analysis of functional MRI data has revealed that brain regions can be grouped into stable "networks" or communities. In many instances, the communities are characterized as relatively disjoint. Although recent work indicates that brain regions may participate in multiple communities (for example, hub regions), the extent of community overlap is poorly understood. To address these i...

This paper presents a novel spectral algorithm with additive clustering, designed to identify overlapping communities in networks. The algorithm is based on geometric properties of the spectrum of the expected adjacency matrix in a random graph model that we call stochastic blockmodel with overlap (SBMO). An adaptive version of the algorithm, that does not require the knowledge of the number of...

A lot of complex systems in nature and society can be represented as the form of network. The small-scale subnets topological features are vital to understand the dynamics and function of the networks. Triangles comprised of three nodes are the simplest subnet in the network. Based on the triangle distribution of the complex network, we present a novel approach to detect overlapping community s...

Detect overlapping communities efficiently and effectively in various social networks has been more and more important. Aiming at the high complexity of expanding strategy and the defect of generating many homeless nodes in LFM, we propose a quick algorithm based on local optimization of a fitness function(QLFM). The proposed algorithm firstly select a node as seed randomly .With a local fitnes...