Papers › GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
Tianhao Peng, Wenjun Wu, Haitao Yuan, Zhifeng Bao, Zhao Pengrui, Xin Yu, Xuetao Lin, Yu Liang, Yanjun Pu
Graph neural networks (GNNs) have shown advantages in graph-based analysis tasks. However, most existing methods have the homogeneity assumption and show poor performance on heterophilic graphs, where the linked nodes have dissimilar features and different class labels, and the semantically related nodes might be multi-hop away. To address this limitation, this paper presents GraphRARE, a general framework built upon node relative entropy and deep reinforcement learning, to strengthen the expressive capability of GNNs. An innovative node relative entropy, which considers node features and structural similarity, is used to measure mutual information between node pairs. In addition, to avoid the sub-optimal solutions caused by mixing useful information and noises of remote nodes, a deep reinforcement learning-based algorithm is developed to optimize the graph topology. This algorithm selects informative nodes and discards noisy nodes based on the defined node relative entropy. Extensive experiments are conducted on seven real-world datasets. The experimental results demonstrate the superiority of GraphRARE in node classification and its capability to optimize the original graph topology.
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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 | Chameleon | GraphSAGE-RARE (λ=1.0) | Accuracy | 69.28±1.90 | #33 of 61 | Archive leaderboard | report |
| Node Classification | Cornell | H2GCN-RARE (λ=1.0) | Accuracy | 87.84±4.05 | #4 of 60 | Archive leaderboard | report |
| Node Classification | Squirrel | GCN-RARE (λ=1.0) | Accuracy | 55.90±1.39 | #35 of 59 | Archive leaderboard | report |
| Node Classification | Texas | H2GCN-RARE (λ=1.0) | Accuracy | 86.76±5.80 | #17 of 62 | Archive leaderboard | report |
| Node Classification | Wisconsin | H2GCN-RARE (λ=1.0) | Accuracy | 90.00±2.97 | #4 of 63 | 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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