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As the availability and importance of relational data—such as the friendships summarized on a social networking website—increases, it becomes increasingly important to have good models for such data. The kinds of latent structure that have been considered for use in predicting links in such networks have been relatively limited. In particular, the machine(More)
We describe a flexible nonparametric approach to latent variable modelling in which the number of latent variables is unbounded. This approach is based on a probability distribution over equivalence classes of binary matrices with a finite number of rows, corresponding to the data points, and an unbounded number of columns, corresponding to the latent(More)
Nonparametric Bayesian models are often based on the assumption that the objects being modeled are exchangeable. While appropriate in some applications (e.g., bag-of-words models for documents), exchangeabil-ity is sometimes assumed simply for computational reasons; non-exchangeable models might be a better choice for applications based on subject matter.(More)
In recent years, several Bayesian models have been developed for link prediction in relational data. Models such as the Infinite Relational Model (IRM) [3] and the Mixed-Membership Stochastic Blockmodel (MMSB) [1] assume that there exists a set of latent classes that each object we observe can belong to and that each object either belongs to a single class(More)
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