Papers › Improving the Effective Receptive Field of Message-Passing Neural Networks
Improving the Effective Receptive Field of Message-Passing Neural Networks
Shahaf E. Finder, Ron Shapira Weber, Moshe Eliasof, Oren Freifeld, Eran Treister
Message-Passing Neural Networks (MPNNs) have become a cornerstone for processing and analyzing graph-structured data. However, their effectiveness is often hindered by phenomena such as over-squashing, where long-range dependencies or interactions are inadequately captured and expressed in the MPNN output. This limitation mirrors the challenges of the Effective Receptive Field (ERF) in Convolutional Neural Networks (CNNs), where the theoretical receptive field is underutilized in practice. In this work, we show and theoretically explain the limited ERF problem in MPNNs. Furthermore, inspired by recent advances in ERF augmentation for CNNs, we propose an Interleaved Multiscale Message-Passing Neural Networks (IM-MPNN) architecture to address these problems in MPNNs. Our method incorporates a hierarchical coarsening of the graph, enabling message-passing across multiscale representations and facilitating long-range interactions without excessive depth or parameterization. Through extensive evaluations on benchmarks such as the Long-Range Graph Benchmark (LRGB), we demonstrate substantial improvements over baseline MPNNs in capturing long-range dependencies while maintaining computational efficiency.
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Code
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b8c2e5edac5472b4 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Classification | COCO-SP | IM-GatedGCN | macro F1 | 0.3501 ± 0.0061 | #5 of 19 | Archive leaderboard | report |
| Node Classification | COCO-SP | IM-GCN | macro F1 | 0.1960 ± 0.0023 | #15 of 19 | Archive leaderboard | report |
| Node Classification | Log Angeles | IM-GCN | Average Top-1 Accuracy | 62.4 ± 0.3 | #1 of 5 | Archive leaderboard | report |
| Node Classification | London | IM-GCN | Average Top-1 Accuracy | 58.9 ± 0.1 | #1 of 5 | Archive leaderboard | report |
| Node Classification | Paris | IM-GCN | Average Top-1 Accuracy | 55.3 ± 0.3 | #1 of 5 | Archive leaderboard | report |
| Node Classification | PascalVOC-SP | IM-GatedGCN | macro F1 | 0.4332 ± 0.0078 | #4 of 21 | Archive leaderboard | report |
| Node Classification | PascalVOC-SP | IM-GCN | macro F1 | 0.2929 ± 0.0058 | #13 of 21 | Archive leaderboard | report |
| Node Classification | Shanghai | IM-GCN | Average Top-1 Accuracy | 67.8 ± 0.1 | #1 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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