Summary of Fine-grained Expressive Power Of Weisfeiler-leman: a Homomorphism Counting Perspective, by Junru Zhou et al.
Fine-Grained Expressive Power of Weisfeiler-Leman: A Homomorphism Counting Perspective
by Junru Zhou, Muhan Zhang
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Discrete Mathematics (cs.DM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A unified framework for evaluating the expressive power of graph neural networks (GNNs) is proposed by generalizing Weisfeiler-Leman algorithms. The authors introduce Generalized Folklore Weisfeiler-Leman (GFWL) as a flexible design basis for powerful GNNs, and provide a theoretical framework to determine the homomorphism counting power of any class of GNN within this design space. This greatly extends existing works on evaluating GNN expressiveness, and may facilitate automated GNN model design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph neural networks (GNNs) can count homomorphisms, a measure of their expressive power. Researchers have studied specific types of GNNs but lacked a simple way to analyze the problem. This paper proposes a new approach called Generalized Folklore Weisfeiler-Leman (GFWL). It helps determine how powerful any type of GNN is by counting homomorphisms. This can be useful for designing better GNN models. |
Keywords
* Artificial intelligence * Gnn
