Large-scale entity resolution via microclustering Ewens--Pitman random partitions

Mario Beraha; Stefano Favaro

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arXiv · 2507.18101

Large-scale entity resolution via microclustering Ewens--Pitman random partitions

Mario Beraha·Stefano Favaro

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Abstract

We introduce the microclustering Ewens--Pitman model for random partitions, obtained by scaling the strength parameter of the Ewens--Pitman model linearly with the sample size. The resulting random partition is shown to have the microclustering property, namely: the size of the largest cluster grows sub-linearly with the sample size, while the number of clusters grows linearly. By leveraging the interplay between the Ewens--Pitman random partition with the Pitman--Yor process, we develop efficient variational inference schemes for posterior computation in entity resolution. Our approach achieves a speed-up of three orders of magnitude over existing Bayesian methods for entity resolution, while maintaining competitive empirical performance.

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BibTeX RIS

Mario Beraha, Stefano Favaro. 2025-07-24. Large-scale entity resolution via microclustering Ewens--Pitman random partitions. https://arxiv.org/abs/2507.18101

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