The last years, there has been a great interest in detecting overlapping communities in complex networks, which is understood as dense groups of nodes featuring a low outbound density. To date, most methods used to uncover overlapping communities stem from the field of disjoint community detection by attempting to decompose the whole network into several possibly overlapping groups of nodes. In...

The last years, there has been a great interest in detecting overlapping communities in complex networks, which is understood as dense groups of nodes featuring a low outbound density. To date, most methods used to uncover overlapping communities stem from the field of disjoint community detection by attempting to decompose the whole network into several possibly overlapping groups of nodes. In...

Abstract We present a Bayesian nonparametric Poisson factorization model for modeling dense network data with an unknown and potentially growing number of overlapping communities. The construction is based on completely random measures allows the communities to either increase nodes at specified logarithmic or polynomial rate, be bounded. develop asymptotics size derive Markov chain Monte Carlo...

We propose an algorithm for detecting communities of links in networks which uses local information, is based on a new evaluation function, and allows for pervasive overlaps of communities. The complexity of the clustering task requires the application of a memetic algorithm that combines probabilistic evolutionary strategies with deterministic local searches. In Part 2 we will present results ...