Papers › Residual Network and Embedding Usage: New Tricks of Node Classification with Graph...
Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional Networks
Huixuan Chi, Yuying Wang, Qinfen Hao, Hong Xia
Graph Convolutional Networks (GCNs) and subsequent variants have been proposed to solve tasks on graphs, especially node classification tasks. In the literature, however, most tricks or techniques are either briefly mentioned as implementation details or only visible in source code. In this paper, we first summarize some existing effective tricks used in GCNs mini-batch training. Based on this, two novel tricks named GCN_res Framework and Embedding Usage are proposed by leveraging residual network and pre-trained embedding to improve baseline's test accuracy in different datasets. Experiments on Open Graph Benchmark (OGB) show that, by combining these techniques, the test accuracy of various GCNs increases by 1.21%~2.84%. We open source our implementation at https://github.com/ytchx1999/PyG-OGB-Tricks.
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 | GAT-node2vec + BoT + self-KD | Ext. data | No | #23 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | GAT-node2vec + BoT + self-KD | Number of params | 1700432 | #23 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | GAT-node2vec + BoT + self-KD | Test Accuracy | 0.7420 ± 0.0004 | #23 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | GAT-node2vec + BoT + self-KD | Validation Accuracy | 0.7482 ± 0.0015 | #23 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | GAT-node2vec + BoT | Ext. data | No | #28 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | GAT-node2vec + BoT | Number of params | 1700432 | #28 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | GAT-node2vec + BoT | Test Accuracy | 0.7405 ± 0.0004 | #28 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-arxiv | GAT-node2vec + BoT | Validation Accuracy | 0.7482 ± 0.0015 | #28 of 86 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | R-GSN + metapath2vec | Ext. data | No | #24 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | R-GSN + metapath2vec | Number of params | 309777252 | #24 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | R-GSN + metapath2vec | Test Accuracy | 0.5109 ± 0.0038 | #24 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | R-GSN + metapath2vec | Validation Accuracy | 0.5295 ± 0.0042 | #24 of 39 | 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.
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