Papers › Next Level Message-Passing with Hierarchical Support Graphs

Next Level Message-Passing with Hierarchical Support Graphs

22 Jun 2024arXiv:2406.15852archive 2025-07-28

Carlos Vonessen, Florian Grötschla, Roger Wattenhofer

Message-Passing Neural Networks (MPNNs) are extensively employed in graph learning tasks but suffer from limitations such as the restricted scope of information exchange, by being confined to neighboring nodes during each round of message passing. Various strategies have been proposed to address these limitations, including incorporating virtual nodes to facilitate global information exchange. In this study, we introduce the Hierarchical Support Graph (HSG), an extension of the virtual node concept created through recursive coarsening of the original graph. This approach provides a flexible framework for enhancing information flow in graphs, independent of the specific MPNN layers utilized. We present a theoretical analysis of HSGs, investigate their empirical performance, and demonstrate that HSGs can surpass other methods augmented with virtual nodes, achieving state-of-the-art results across multiple datasets.

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default carlosinator/support-graphs/graphgps/layer/performer_layer.py official repository ran · violated contract fingerprinted MIT (permissive) · 60fff7c3c400d7ff · report
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exists carlosinator/support-graphs/graphgps/layer/performer_layer.py official repository ran · violated contract MIT (permissive) · aa5486a3650902d8 · report
get_final_pretrained_ckpt carlosinator/support-graphs/graphgps/finetuning.py official repository ran MIT (permissive) · 1bb331bf70a78136 · report
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init_model_from_pretrained carlosinator/support-graphs/graphgps/finetuning.py official repository unverified MIT (permissive) · 3db4474abe13fdee · report
is_seed carlosinator/support-graphs/graphgps/agg_runs.py official repository unverified MIT (permissive) · c98a702be0660ac9 · report
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load_pretrained_model_cfg carlosinator/support-graphs/graphgps/finetuning.py official repository unverified MIT (permissive) · d796cdea5889c847 · report

Tasks

Graph ClassificationGraph LearningGraph Property PredictionGraph RegressionNode Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Graph Classification Peptides-func GatedGCN-HSG AP 0.6866±0.0038 #18 of 44 Archive leaderboard report
Graph Property Prediction ogbg-molpcba GatedGCN-HSG Test AP 0.3129±0.0020 #7 of 36 Archive leaderboard report
Graph Regression Peptides-struct GatedGCN-HSG MAE 0.2421±0.0007 #2 of 39 Archive leaderboard report
Node Classification COCO-SP GatedGCN-HSG macro F1 0.3535±0.0032 #4 of 19 Archive leaderboard report
Node Classification PascalVOC-SP GatedGCN-HSG macro F1 0.4604±0.0059 #2 of 21 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

MPNN

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