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.
@article{arxiv.2312.11927,
title = {Empowering Dual-Level Graph Self-Supervised Pretraining with Motif Discovery},
author = {Pengwei Yan and Kaisong Song and Zhuoren Jiang and Yangyang Kang and Tianqianjin Lin and Changlong Sun and Xiaozhong Liu},
journal= {arXiv preprint arXiv:2312.11927},
year = {2023}
}