A graphical model for multi-relational social network analysis
نویسنده
چکیده
In this paper, we propose a graphical model for multi-relational social network analysis based on latent variable models. Latent variable models are one of the successful approaches for social network analysis. These models assume a latent variable for each entity and then the probability distribution over relationships between entities is modeled via a function over latent variables. Here, we use latent feature networks (LFN) — a general purpose framework for multi-relation learning via latent variable models. The experimental results show that using the side information via the proposed model can drastically improve the link prediction task in a social network.
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