Estimation of a Low-rank Topic-Based Model for Information Cascades
This work addresses the challenge of inferring social network dynamics from incomplete cascade data, offering a more interpretable approach for researchers in network analysis and information diffusion.
The authors tackled the problem of estimating latent social network structure from observed information diffusion events without known infection sources, by proposing a low-rank topic-based model that incorporates node influence and receptivity vectors. Their model demonstrated improved performance and better interpretability compared to state-of-the-art methods in experiments on synthetic and real data.
We consider the problem of estimating the latent structure of a social network based on the observed information diffusion events, or cascades, where the observations for a given cascade consist of only the timestamps of infection for infected nodes but not the source of the infection. Most of the existing work on this problem has focused on estimating a diffusion matrix without any structural assumptions on it. In this paper, we propose a novel model based on the intuition that an information is more likely to propagate among two nodes if they are interested in similar topics which are also prominent in the information content. In particular, our model endows each node with an influence vector (which measures how authoritative the node is on each topic) and a receptivity vector (which measures how susceptible the node is for each topic). We show how this node-topic structure can be estimated from the observed cascades, and prove the consistency of the estimator. Experiments on synthetic and real data demonstrate the improved performance and better interpretability of our model compared to existing state-of-the-art methods.