  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.
