Papers › GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
Fenyu Hu, Yanqiao Zhu, Shu Wu, Weiran Huang, Liang Wang, Tieniu Tan
Graph representation learning is of paramount importance for a variety of graph analytical tasks, ranging from node classification to community detection. Recently, graph convolutional networks (GCNs) have been successfully applied for graph representation learning. These GCNs generate node representation by aggregating features from the neighborhoods, which follows the "neighborhood aggregation" scheme. In spite of having achieved promising performance on various tasks, existing GCN-based models have difficulty in well capturing complicated non-linearity of graph data. In this paper, we first theoretically prove that coefficients of the neighborhood interacting terms are relatively small in current models, which explains why GCNs barely outperforms linear models. Then, in order to better capture the complicated non-linearity of graph data, we present a novel GraphAIR framework which models the neighborhood interaction in addition to neighborhood aggregation. Comprehensive experiments conducted on benchmark tasks including node classification and link prediction using public datasets demonstrate the effectiveness of the proposed method.
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Code
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Code Syntology ran Syntology
11 samples harvested; 7 ran; 3 honoured the contract we drafted; 4 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 | AIR-GCN | Accuracy | 72.9% | #21 of 40 | Archive leaderboard | report |
| Node Classification | Cora with Public Split: fixed 20 nodes per class | AIR-GCN | Accuracy | 84.7% | #7 of 36 | Archive leaderboard | report |
| Node Classification | PubMed with Public Split: fixed 20 nodes per class | AIR-GCN | Accuracy | 80% | #17 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.
Methods
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