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We consider the problem of estimating the latent structure of a socialnetwork based on the observed information diffusion events, or cascades, wherethe observations for a given cascade consist of only the timestamps ofinfection for infected nodes but not the source of the infection. Most of theexisting work on this problem has focused on estimating a diffusion matrixwithout any structural assumptions on it. In this paper, we propose a novelmodel based on the intuition that an information is more likely to propagateamong two nodes if they are interested in similar topics which are alsoprominent in the information content. In particular, our model endows each nodewith an influence vector (which measures how authoritative the node is on eachtopic) and a receptivity vector (which measures how susceptible the node is foreach topic). We show how this node-topic structure can be estimated from theobserved cascades, and prove the consistency of the estimator. Experiments onsynthetic and real data demonstrate the improved performance and betterinterpretability of our model compared to existing state-of-the-art methods.

Estimation of a Low-rank Topic-Based Model for Information Cascades