Summary of Homomorphism Counts For Graph Neural Networks: All About That Basis, by Emily Jin et al.
Homomorphism Counts for Graph Neural Networks: All About That Basis
by Emily Jin, Michael Bronstein, İsmail İlkan Ceylan, Matthias Lanzinger
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper presents a novel approach to improving the expressiveness of graph neural networks, which are limited in their ability to count certain patterns in graphs. Two existing methods attempt to address this limitation by incorporating subgraph or homomorphism pattern counts into graph features. However, these approaches have been shown to be sub-optimal. Instead, the authors propose a more fine-grained method that incorporates the homomorphism counts of all structures in the basis of the target pattern. This approach yields strictly more expressive architectures without increasing computational complexity. Theoretical results are proven on node-level and graph-level motif parameters, and empirical validation is performed on standard benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make graph neural networks better at counting patterns in graphs. Some people thought that adding certain kinds of information to the graphs would help with this. But it turns out those ideas aren’t as good as they seemed. Instead, the authors came up with a new idea that counts all sorts of patterns and uses that to make the network smarter. This new approach is really good at doing what the old approaches couldn’t do, without using any more computer power. |
