Expectation Complete Graph Representations using Graph Homomorphisms

Author: P. Welke, M. Thiessen, T. Gärtner
Year: 2022

Citation information

P. Welke, M. Thiessen, T. Gärtner,
New Frontiers in Graph Learning Workshop at NeurIPS (GLFrontiers at NeurIPS),
2022,
https://openreview.net/forum?id=Zf-Mn6xzD2B

We propose and study a practical graph embedding that *in expectation* is able to distinguish all non-isomorphic graphs and can be computed in polynomial time. The embedding is based on Lovász’ characterisation of graph isomorphism through an infinite dimensional vector of homomorphism counts. Recent work has studied the expressiveness of graph embeddings by comparing their ability to distinguish graphs to that of the Weisfeiler-Leman hierarchy. While previous methods have either limited expressiveness or are computationally impractical, we devise efficient sampling-based alternatives that are maximally expressive in expectation. We empirically evaluate our proposed embeddings and show competitive results on several benchmark graph learning tasks.