document: Topic modeling with network regularization In this paper , we formally define the problem of topic modeling with network structure ( TMN ) . We propose a novel solution to this problem , which regularizes a statistical topic model with a harmonic regularizer based on a graph structure in the data . The proposed method bridges topic modeling and social network analysis , which leverages the power of both statistical topic models and discrete regularization . The output of this model well summarizes topics in text , maps a topic on the network , and discovers topical communities . With concrete selection of a topic model and a graph-based regularizer , our model can be applied to text mining problems such as author-topic analysis , community discovery , and spatial text mining . Empirical experiments on two different genres of data show that our approach is effective , which improves text-oriented methods as well as network-oriented methods . The proposed model is general ; it can be applied to any text collections with a mixture of topics and an associated network structure .
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