Latent Community Discovery with Network Regularization for Core Actors Clustering
نویسندگان
چکیده
ABSTRACT Community structure is a common attribute of many social networks, which would give us a better understanding of the networks. However, as the social networks grow lager and lager nowadays, the Pareto Principle becomes more and more notable which makes traditional community discovery algorithms no longer suitable for them. This principle explains the unbalanced existence of two different types of network actors. Specifically, the core actors usually occupy only a small proportion of the population, but have a large influence. In this paper, we propose a novel algorithm LCDN (Latent Community Discovery with Network Structure) for dividing the core actors. This is a hierarchical probabilistic model based on statistical topic model and regularized by network structure in data. We had experiments on three large networks which show that this new model performs much better than the traditional statistical models and network partitioning algorithms.
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