Edge-exchangeable graphs and sparsity

Abstract

Many popular network models rely on the assumption of (vertex) exchangeability, in which the distribution of the graph is invariant to relabelings of the vertices. However, the Aldous-Hoover theorem guarantees that these graphs are dense or empty with probability one, whereas many real-world graphs are sparse. We present an alternative notion of exchangeability for random graphs, which we call edge exchangeability, in which the distribution of a graph sequence is invariant to the order of the edges. We demonstrate that edge-exchangeable models, unlike models that are traditionally vertex exchangeable, can exhibit sparsity. To do so, we outline a general framework for graph generative models; by contrast to the pioneering work of Caron and Fox [12], models within our framework are stationary across steps of the graph sequence. In particular, our model grows the graph by instantiating more latent atoms of a single random measure as the dataset size increases, rather than adding new atoms to the measure.

Publication
Advances in Neural Information Processing Systems (NeurIPS)

Preliminary versions appeared as:

  • Completely random measures for modeling power laws in sparse graphs. NIPS workshop on Networks in the Social and Information Sciences, 2015.
  • Edge-exchangeable graphs and sparsity. NIPS workshop on Networks in the Social and Information Sciences, 2015.
  • Edge-exchangeable graphs, sparsity, and power laws. NIPS Workshop on Bayesian Nonparametrics: The Next Generation, 2015.

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Diana Cai

I am broadly interested in probabilistic modeling, and in particular, theory and methods for robust, scalable, and nonparametric Bayesian modeling.