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arXiv · 2312.11927

Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery

Pengwei Yan·Kaisong Song·Zhuoren Jiang·Yangyang Kang·Tianqianjin Lin·Changlong Sun·Xiaozhong Liu
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Abstract

While self-supervised graph pretraining techniques have shown promising results in various domains, their application still experiences challenges of limited topology learning, human knowledge dependency, and incompetent multi-level interactions. To address these issues, we propose a novel solution, Dual-level Graph self-supervised Pretraining with Motif discovery (DGPM), which introduces a unique dual-level pretraining structure that orchestrates node-level and subgraph-level pretext tasks. Unlike prior approaches, DGPM autonomously uncovers significant graph motifs through an edge pooling module, aligning learned motif similarities with graph kernel-based similarities. A cross-matching task enables sophisticated node-motif interactions and novel representation learning. Extensive experiments on 15 datasets validate DGPM's effectiveness and generalizability, outperforming state-of-the-art methods in unsupervised representation learning and transfer learning settings. The autonomously discovered motifs demonstrate the potential of DGPM to enhance robustness and interpretability.

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Pengwei Yan, Kaisong Song, Zhuoren Jiang, Yangyang Kang, Tianqianjin Lin, Changlong Sun, Xiaozhong Liu. 2023-12-19. Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery. https://arxiv.org/abs/2312.11927

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