Computing Expected Motif Counts for Exchangeable Graph Generative Models
This work addresses a computational bottleneck for researchers and practitioners using graph models, but it appears incremental as it builds on existing mixture model frameworks.
The paper tackles the problem of estimating expected motif counts for exchangeable graph generative models, presenting a scalable estimation procedure applicable to mixture models used in neural and Bayesian graph data approaches.
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.