Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866909187057909760 |
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| author | Alvarez-Gonzalez, Nurudin Kaltenbrunner, Andreas Gómez, Vicenç |
| author_facet | Alvarez-Gonzalez, Nurudin Kaltenbrunner, Andreas Gómez, Vicenç |
| contents | We present a novel edge-level ego-network encoding for learning on graphs that can boost Message Passing Graph Neural Networks (MP-GNNs) by providing additional node and edge features or extending message-passing formats. The proposed encoding is sufficient to distinguish Strongly Regular Graphs, a family of challenging 3-WL equivalent graphs. We show theoretically that such encoding is more expressive than node-based sub-graph MP-GNNs. In an empirical evaluation on four benchmarks with 10 graph datasets, our results match or improve previous baselines on expressivity, graph classification, graph regression, and proximity tasks -- while reducing memory usage by 18.1x in certain real-world settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_05905 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings Alvarez-Gonzalez, Nurudin Kaltenbrunner, Andreas Gómez, Vicenç Machine Learning Artificial Intelligence We present a novel edge-level ego-network encoding for learning on graphs that can boost Message Passing Graph Neural Networks (MP-GNNs) by providing additional node and edge features or extending message-passing formats. The proposed encoding is sufficient to distinguish Strongly Regular Graphs, a family of challenging 3-WL equivalent graphs. We show theoretically that such encoding is more expressive than node-based sub-graph MP-GNNs. In an empirical evaluation on four benchmarks with 10 graph datasets, our results match or improve previous baselines on expressivity, graph classification, graph regression, and proximity tasks -- while reducing memory usage by 18.1x in certain real-world settings. |
| title | Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2312.05905 |