Papers › GTNet: A Tree-Based Deep Graph Learning Architecture
GTNet: A Tree-Based Deep Graph Learning Architecture
Nan Wu, Chaofan Wang
We propose Graph Tree Networks (GTNets), a deep graph learning architecture with a new general message passing scheme that originates from the tree representation of graphs. In the tree representation, messages propagate upward from the leaf nodes to the root node, and each node preserves its initial information prior to receiving information from its child nodes (neighbors). We formulate a general propagation rule following the nature of message passing in the tree to update a node's feature by aggregating its initial feature and its neighbor nodes' updated features. Two graph representation learning models are proposed within this GTNet architecture - Graph Tree Attention Network (GTAN) and Graph Tree Convolution Network (GTCN), with experimentally demonstrated state-of-the-art performance on several popular benchmark datasets. Unlike the vanilla Graph Attention Network (GAT) and Graph Convolution Network (GCN) which have the "over-smoothing" issue, the proposed GTAN and GTCN models can go deep as demonstrated by comprehensive experiments and rigorous theoretical analysis.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Property Prediction | ogbn-arxiv | GTAN | Ext. data | No | #52 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | GTAN | Number of params | 39208 | #52 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | GTAN | Test Accuracy | 0.7297 ± 0.0017 | #52 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | GTAN | Validation Accuracy | 0.7384 ± 0.0007 | #52 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | GTCN | Ext. data | No | #62 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | GTCN | Number of params | 109096 | #62 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | GTCN | Test Accuracy | 0.7225 ± 0.0017 | #62 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | GTCN | Validation Accuracy | 0.7320 ± 0.0005 | #62 of 86 | 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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