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...

In complex networks it is common for each node to belong to several communities, implying a highly overlapping community structure. Recent advances in benchmarking indicate that the existing community assignment algorithms that are capable of detecting overlapping communities perform well only when the extent of community overlap is kept to modest levels. To overcome this limitation, we introdu...

In this paper, we study the problem of identifying misbehaving network communications using community detection algorithms. Recently, it was shown that identifying the communications that do not respect community boundaries is a promising approach for network intrusion detection. However, it was also shown that traditional community detection algorithms are not suitable for this purpose. In thi...

[This corrects the article DOI: 10.1155/2014/105428.].

How can we approximate sparse graphs and sequences of sparse graphs (with average degree unbounded and o(n))? We consider convergence in the first k moments of the graph spectrum (equivalent to the numbers of closed k-walks) appropriately normalized. We introduce a simple, easy to sample, random graph model that captures the limiting spectra of many sequences of interest, including the sequence...

Overlapping community detection has drawn much attention recently since it allows nodes in a network to have multiple community memberships. A standard framework to deal with overlapping community detection is Matrix Factorization (MF). Although all existing MF-based approaches use links as input to identify communities, the relationship between links and communities is still under-investigated...