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.

我们提出了一种新颖的解决方案，双层图自监督预训练与图形模体发现（DGPM），通过引入独特的双层预训练结构来解决拓扑学习有限，依赖人类知识和多级交互无力等挑战。在 15 个数据集上的大量实验证实了 DGPM 的有效性和普适性，在无监督表示学习和迁移学习设置中优于最先进的方法。自主发现的模体展示了 DGPM 提升鲁棒性和可解释性的潜力。