Papers › Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
Fenyu Hu, Yanqiao Zhu, Shu Wu, Liang Wang, Tieniu Tan
Graph convolutional networks (GCNs) have been successfully applied in node classification tasks of network mining. However, most of these models based on neighborhood aggregation are usually shallow and lack the "graph pooling" mechanism, which prevents the model from obtaining adequate global information. In order to increase the receptive field, we propose a novel deep Hierarchical Graph Convolutional Network (H-GCN) for semi-supervised node classification. H-GCN first repeatedly aggregates structurally similar nodes to hyper-nodes and then refines the coarsened graph to the original to restore the representation for each node. Instead of merely aggregating one- or two-hop neighborhood information, the proposed coarsening procedure enlarges the receptive field for each node, hence more global information can be captured. The proposed H-GCN model shows strong empirical performance on various public benchmark graph datasets, outperforming state-of-the-art methods and acquiring up to 5.9% performance improvement in terms of accuracy. In addition, when only a few labeled samples are provided, our model gains substantial improvements.
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Code
Syntology Ran 8 of 13 code samples harvested from 1 repository linked to this paper; 5 have no recorded run. Of those that ran: 3 ran · honoured contract; 2 ran · violated contract; 1 ran · our draft was wrong; 1 ran · fixture could not drive it; 1 ran with no contract checked.
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Code Syntology ran Syntology
13 samples harvested; 8 ran; 3 honoured the contract we drafted; 5 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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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 with Public Split: fixed 20 nodes per class | H-GCN | Accuracy | 72.8% | #23 of 40 | Archive leaderboard | report |
| Node Classification | Cora with Public Split: fixed 20 nodes per class | H-GCN | Accuracy | 84.5% | #8 of 36 | Archive leaderboard | report |
| Node Classification | PubMed with Public Split: fixed 20 nodes per class | H-GCN | Accuracy | 79.8% | #19 of 37 | 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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