document: Probabilistic topic models with biased propagation on heterogeneous information networks With the development of Web applications , textual documents are not only getting richer , but also ubiquitously interconnected with users and other objects in various ways , which brings about text-rich heterogeneous information networks . Topic models have been proposed and shown to be useful for document analysis , and the interactions among multi-typed objects play a key role at disclosing the rich semantics of the network . However , most of topic models only consider the textual information while ignore the network structures or can merely integrate with homogeneous networks . None of them can handle heterogeneous information network well . In this paper , we propose a novel topic model with biased propagation ( TMBP ) algorithm to directly incorporate heterogeneous information network with topic modeling in a unified way . The underlying intuition is that multi-typed objects should be treated differently along with their inherent textual information and the rich semantics of the heterogeneous information network . A simple and unbiased topic propagation across such a heterogeneous network does not make much sense . Consequently , we investigate and develop two biased propagation frameworks , the biased random walk framework and the biased regularization framework , for the TMBP algorithm from different perspectives , which can discover latent topics and identify clusters of multi-typed objects simultaneously . We extensively evaluate the proposed approach and compare to the state-of-the-art techniques on several datasets . Experimental results demonstrate that the improvement in our proposed approach is consistent and promising .
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