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

• URL: http://arxiv.org/abs/2312.11927v1
• Date: Tue, 19 Dec 2023 08:09:36 GMT
• Title: Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery
• Authors: Pengwei Yan, Kaisong Song, Zhuoren Jiang, Yangyang Kang, Tianqianjin Lin, Changlong Sun, Xiaozhong Liu
• Abstract summary: We introduce Dual-level Graph self-supervised Pretraining with Motif discovery (DGPM) DGPM orchestrates node-level and subgraph-level pretext tasks. Experiments on 15 datasets validate DGPM's effectiveness and generalizability.
• Score: 28.38130326794833
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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