Papers › Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification

Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification

13 Feb 2019arXiv:1902.06667archive 2025-07-28

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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Tasks

ClassificationGeneral ClassificationNode Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

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