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BNEM: a fast community detection algorithm using generative models

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Actors in social networks tend to form community groups based on common location, interests, occupation, etc. Communities play special roles in the structure–function relationship; therefore, detecting such communities can be a way to describe and analyze such networks. However, the size of those networks has grown tremendously with the increase of computational power and data storage. While various methods have been developed to extract community structures, their computational cost or the difficulty to parallelize existing algorithms make partitioning real networks into communities a challenging problem. In this paper, we introduce a generative process to model the interactions between social network’s actors. Through unsupervised learning using expectation maximization, we derive an efficient and fast community detection algorithm based on Bayesian network and expectation maximization (BNEM). We show that BNEM algorithm can infer communities within directed or undirected networks, and within weighted or un-weighted networks. We also show that the algorithm is easy to parallelize. We then explore and analyze the result of the BNEM method. Finally, we conduct a comparative analysis with other well-known methods in the fields of community detection.

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  1. A discrete distribution (Discrete (\(\theta\)))—generalized Bernoulli distribution—is a probability distribution that describes the result of a random event that can take on one of \(c\) possible outcomes, with the probability of each outcome separately specified with the vector parameter \(\theta\).


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Correspondence to Ahmed Ibrahem Hafez.

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Hafez, A.I., Hassanien, A.E. & Fahmy, A.A. BNEM: a fast community detection algorithm using generative models. Soc. Netw. Anal. Min. 4, 226 (2014).

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