Computing Expected Motif Counts for Exchangeable Graph Generative Models

Oliver Schulte
5/1/2023
cs.LGcs.AI

No confident code implementation found for this paper.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Studying and Improving Graph Neural Network-based Motif Estimation

81% match2025

Goodness-of-Fit of Attributed Probabilistic Graph Generative Models

79% match2023

From Moments to Models: Graphon Mixture-Aware Mixup and Contrastive Learning

78% match2025

On the Role of Edge Dependency in Graph Generative Models

78% match2023

From Graph Generation to Graph Classification

77% match2023

PolyGraph Discrepancy: a classifier-based metric for graph generation

77% match1 repo132025

Cite this paper

@article{schulte2023computing,
  title  = {Computing Expected Motif Counts for Exchangeable Graph Generative Models},
  author = {Oliver Schulte},
  year   = {2023},
  eprint = {2305.01089},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2305.01089v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2305.01089.svg)](https://codeofpaper.com/paper/2305.01089)

Discussion