Multi-Scale Contrast for Global-Interaction Graph Representation Learning

Recently, significant strides have been made in the field of representation learning. Nevertheless, prior methods have predominantly centered on supervised learning, which necessitates a reliance on costly labeled datasets. In response to this challenge, graph contrastive learning leverages the potential of unlabeled data by maximizing the consistency between similar graph pairs, extracting effective representations of graph data. However, existing methods lack the consideration of multi-scale information and tend to focus on extracting local graph features, especially overlooking the interdependencies between graph-level representations. To overcome these challenges, we present an innovative framework, Multi-Scale Contrast for Global-Interaction Graph Representation Learning (MSCGI) to capture more abundant features. Firstly, our framework models the given graph at multiple scales, with subgraphs at each scale encoded by independent encoders. Subsequently, we design a Global Interaction module that generates edges among graphs based on the similarity of graph-level representations, thereby constructing a “global graph” to capture the interrelations among graphs. Finally, the framework maximizes the mutual information across the various scales to capture hierarchical information. Comprehensive experiments demonstrate that MSCGI outperforms the state-of-the-art unsupervised methods.