https://github.com/borgwardtlab/gnnscancountsubstructures
Code and Data for the paper: "Graph Neural Networks Can (Often) Count Substructures" (ICLR 2025 Spotlight)
https://github.com/borgwardtlab/gnnscancountsubstructures
gnns iclr2025 subgraph-isomorphism
Last synced: 2 months ago
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Code and Data for the paper: "Graph Neural Networks Can (Often) Count Substructures" (ICLR 2025 Spotlight)
- Host: GitHub
- URL: https://github.com/borgwardtlab/gnnscancountsubstructures
- Owner: BorgwardtLab
- License: mit
- Created: 2025-02-27T11:22:42.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-02-27T12:50:41.000Z (3 months ago)
- Last Synced: 2025-02-27T15:52:27.174Z (3 months ago)
- Topics: gnns, iclr2025, subgraph-isomorphism
- Language: GLSL
- Homepage:
- Size: 188 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Graph Neural Networks Can (Often) Count Substructures
This repository contains the experiments for the _ICLR 2025_ (Spotlight) paper "Graph Neural Networks Can (Often) Count Substructures". The paper is available [here](https://openreview.net/forum?id=sZQRUrvLn4).
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|:--|
| *On the left, a graph **G** and a tree pattern **P**. The copies of **G** on the right have colors obtained from one WL iteration. The subgraph isomorphism from **P** to the red-highlighted subgraph of **G** is not quite-colorful, while the one to the green-highlighted subgraph of **G** is quite-colorful (see Section 5 of the paper).*|
### Description
Message passing graph neural networks (GNNs) are known to have limited expressive power in their ability to distinguish some non-isomorphic graphs.
Because of this, it is well known that they are unable to detect or count arbitrary graph substructures (i.e., solving the subgraph isomorphism problem), a task that is of great importance for several types of graph-structured data.
However, we observe that GNNs are in fact able to count graph patterns quite accurately across several real-world graph datasets.
Motivated by this observation, we provide an analysis of the subgraph-counting capabilities of GNNs beyond the worst case, deriving several sufficient conditions for GNNs to be able to count subgraphs and, more importantly, to be able to _sample-efficiently learn_ to count subgraphs.
Moreover, we develop novel dynamic programming algorithms for solving the subgraph isomorphism problem on restricted classes of pattern and target graphs, and show that message-passing GNNs can efficiently simulate these dynamic programs.
### Citing our work
> Paolo Pellizzoni, Till Hendrik Schulz, and Karsten Borgwardt. _Graph Neural Networks Can (Often) Count Substructures_, in ICLR, 2025.
```
@inproceedings{
pellizzoni2025graph,
title={Graph Neural Networks Can (Often) Count Substructures},
author={Paolo Pellizzoni and Till Hendrik Schulz and Karsten Borgwardt},
booktitle={International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=sZQRUrvLn4}
}
```
### Usage
Run ```source s``` to load modules
- dataset_statistics/ contains the experiments to compute the dataset statistics (Section 6.1)
- gnns/ contains the experiments on GNNs (Table 1 and Table 4)