Computing Expected Motif Counts for Exchangeable Graph Generative Models
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Abstract (arXiv)
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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References · 6
- 2016Variational Graph Auto-Encoderscited by 1,373
- 2020Improving Graph Neural Network Expressivity via Subgraph Isomorphism Countingcited by 312
- 2021Evaluation Metrics for Graph Generative Models: Problems, Pitfalls, and Practical Solutionscited by 38
- 2013Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structurescited by 22
- 2020Learning Bayesian Networks Under Sparsity Constraints: A Parameterized Complexity Analysiscited by 13
- 2022Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoderscited by 8
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