Papers › GIPA: General Information Propagation Algorithm for Graph Learning
GIPA: General Information Propagation Algorithm for Graph Learning
Qinkai Zheng, Houyi Li, Peng Zhang, Zhixiong Yang, Guowei Zhang, Xintan Zeng, Yongchao Liu
Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation. In this paper, we present a new graph attention neural network, namely GIPA, for attributed graph data learning. GIPA consists of three key components: attention, feature propagation and aggregation. Specifically, the attention component introduces a new multi-layer perceptron based multi-head to generate better non-linear feature mapping and representation than conventional implementations such as dot-product. The propagation component considers not only node features but also edge features, which differs from existing GNNs that merely consider node features. The aggregation component uses a residual connection to generate the final embedding. We evaluate the performance of GIPA using the Open Graph Benchmark proteins (ogbn-proteins for short) dataset. The experimental results reveal that GIPA can beat the state-of-the-art models in terms of prediction accuracy, e.g., GIPA achieves an average test ROC-AUC of 0.8700±0.0010 and outperforms all the previous methods listed in the ogbn-proteins leaderboard.
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-proteins | GIPA | Ext. data | No | #9 of 26 | Archive leaderboard | report |
| Node Property Prediction | ogbn-proteins | GIPA | Number of params | 4831056 | #9 of 26 | Archive leaderboard | report |
| Node Property Prediction | ogbn-proteins | GIPA | Test ROC-AUC | 0.8700 ± 0.0010 | #9 of 26 | Archive leaderboard | report |
| Node Property Prediction | ogbn-proteins | GIPA | Validation ROC-AUC | 0.9187 ± 0.0003 | #9 of 26 | 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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