Papers › Adaptive Sampling Towards Fast Graph Representation Learning
Adaptive Sampling Towards Fast Graph Representation Learning
Wenbing Huang, Tong Zhang, Yu Rong, Junzhou Huang
Graph Convolutional Networks (GCNs) have become a crucial tool on learning representations of graph vertices. The main challenge of adapting GCNs on large-scale graphs is the scalability issue that it incurs heavy cost both in computation and memory due to the uncontrollable neighborhood expansion across layers. In this paper, we accelerate the training of GCNs through developing an adaptive layer-wise sampling method. By constructing the network layer by layer in a top-down passway, we sample the lower layer conditioned on the top one, where the sampled neighborhoods are shared by different parent nodes and the over expansion is avoided owing to the fixed-size sampling. More importantly, the proposed sampler is adaptive and applicable for explicit variance reduction, which in turn enhances the training of our method. Furthermore, we propose a novel and economical approach to promote the message passing over distant nodes by applying skip connections. Intensive experiments on several benchmarks verify the effectiveness of our method regarding the classification accuracy while enjoying faster convergence speed.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Classification | Citeseer Full-supervised | ASGCN | Accuracy | 79.66% | #2 of 7 | Archive leaderboard | report |
| Node Classification | Cora | AS-GCN | Accuracy | 87.44% ± 0.0034% | #16 of 73 | Archive leaderboard | report |
| Node Classification | Cora Full-supervised | ASGCN | Accuracy | 87.44±0.0034% | #4 of 9 | Archive leaderboard | report |
| Node Classification | Pubmed Full-supervised | ASGCN | Accuracy | 90.6% | #2 of 7 | Archive leaderboard | report |
| Node Classification | ASGCN | Accuracy | 96.27% | #9 of 16 | 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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