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arXiv · 1911.03904

HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network

Deli Chen·Xiaoqian Liu·Yankai Lin·Peng Li·Jie Zhou·Qi Su·Xu Sun
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Abstract

Graph Neural Networks (GNNs) are efficient approaches to process graph-structured data. Modelling long-distance node relations is essential for GNN training and applications. However, conventional GNNs suffer from bad performance in modelling long-distance node relations due to limited-layer information propagation. Existing studies focus on building deep GNN architectures, which face the over-smoothing issue and cannot model node relations in particularly long distance. To address this issue, we propose to model long-distance node relations by simply relying on shallow GNN architectures with two solutions: (1) Implicitly modelling by learning to predict node pair relations (2) Explicitly modelling by adding edges between nodes that potentially have the same label. To combine our two solutions, we propose a model-agnostic training framework named HighwayGraph, which overcomes the challenge of insufficient labeled nodes by sampling node pairs from the training set and adopting the self-training method. Extensive experimental results show that our HighwayGraph achieves consistent and significant improvements over four representative GNNs on three benchmark datasets.

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Deli Chen, Xiaoqian Liu, Yankai Lin, Peng Li, Jie Zhou, Qi Su, Xu Sun. 2019-11-10. HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network. https://arxiv.org/abs/1911.03904

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