Bayesian nonparametric relational learning with the broken tree process

Recently, an increase in the availability and importance of relational datasets-such as social network data or protein interaction data-has lead to increased interest in modelling and learning from such data. Such data are often modelled as exchangeable arrays, yielding a particular representation due to Aldous and Hoover. We present a Bayesian nonparametric model based on this representation, which uses a novel process to generate a partition of the data. We present a Reversible Jump MCMC algorithm for inference in this model, and demonstrate the effectiveness of this approach on real-world data.

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