Spatial compactness meets topical consistency: jointly modeling links and content for community detection

In this paper, we address the problem of discovering topically meaningful, yet compact (densely connected) communities in a social network. Assuming the social network to be an integer-weighted graph (where the weights can be intuitively defined as the number of common friends, followers, documents exchanged, etc.), we transform the social network to a more efficient representation. In this new representation, each user is a bag of her one-hop neighbors. We propose a mixed-membership model to identify compact communities using this transformation. Next, we augment the representation and the model to incorporate user-content information imposing topical consistency in the communities. In our model a user can belong to multiple communities and a community can participate in multiple topics. This allows us to discover community memberships as well as community and user interests. Our method outperforms other well known baselines on two real-world social networks. Finally, we also provide a fast, parallel approximation of the same.

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