Detection of overlapping communities in real-world networks is a generally challenging task. Upon recognizing that a network is in fact the union of its egonets, a novel network representation using multi-way data structures is advocated in this contribution. The introduced sparse tensor-based representation exhibits richer structure compared to its matrix counterpart, and thus enables a more r...

Modern networks are of huge sizes as well as high dynamics, which challenges the efficiency of community detection algorithms. In this paper, we study the problem of overlapping community detection on distributed and dynamic graphs. Given a distributed, undirected and unweighted graph, the goal is to detect overlapping communities incrementally as the graph is dynamically changing. We propose a...

Many networks possess a community structure, such that vertices form densely connected groups which are more sparsely linked to other groups. In some cases these groups overlap, with some vertices shared between two or more communities. Discovering communities in networks is a computationally challenging task, especially if they overlap. In previous work we proposed an algorithm, CONGA, that co...

We discuss two views on extending existing methods for complex network modeling which we dub the communities first and the networks first view, respectively. Inspired by the networks first view that we attribute to White et al.[1], we formulate the multiple-networks stochastic blockmodel (MNSBM), which seeks to separate the observed network into subnetworks of different types and where the prob...

Community detection in networks refers to the process of seeking strongly internally connected groups of nodes which are weakly externally connected. In this work, we introduce and study a community definition based on internal edge density. Beginning with the simple concept that edge density equals number of edges divided by maximal number of edges, we apply this definition to a variety of nod...

We present a new approach to the problem of finding communities: a community is a subset of actors who induce a locally optimal subgraph with respect to a density function defined on subsets of actors. Two different subsets with significant overlap can both be locally optimal, and in this way we may obtain overlapping communities. We design, implement, and test two novel efficient algorithms, R...

Abstract Social networks, as well as many other real-world networks, exhibit overlapping community structure. Affiliation networks, as a large portion of social networks, consist of cooperative individuals: Two individuals are connected by a link if they belong to the same organization(s), such as companies, research groups and hobby clubs. Affiliation networks naturally contain many fully conn...

We analyze a topological structure of networks formed according to the entries and trackbacks in the blogosphere, which is a collection of weblog articles. The analysis is performed based on community extraction, network visualization and keyword analysis. It is shown that the large-scale structure of the blogosphere has a globally sparse, but locally dense structure. The entries in a community...

An important aspect of community analysis is not only determining the communities within the network, but also sub-communities and hierarchies. We present an approach for finding hierarchies in social networks that uses work from random matrix theory to estimate the number of clusters. The method analyzes the spectral fingerprint of the network to determine the level of hierarchy in the network...

Uncovering the overlapping community structure exhibited by real networks is a crucial step toward an understanding of complex systems that goes beyond the local organization of their constituents. Here three fuzzy c-means methods, based on optimal prediction, diffusion distance and dissimilarity index, respectively, are test on two artificial networks, including the widely known ad hoc network...