Computing Expected Motif Counts for Exchangeable Graph Generative Models

Oliver Schulte

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arXiv · 2305.01089

Computing Expected Motif Counts for Exchangeable Graph Generative Models

Oliver Schulte

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Abstract

Estimating the expected value of a graph statistic is an important inference task for using and learning graph models. This note presents a scalable estimation procedure for expected motif counts, a widely used type of graph statistic. The procedure applies for generative mixture models of the type used in neural and Bayesian approaches to graph data.

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Oliver Schulte. 2023-05-01. Computing Expected Motif Counts for Exchangeable Graph Generative Models. https://arxiv.org/abs/2305.01089

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