CLEP: Exploiting Edge Partitioning for Graph Contrastive Learning

Generative and contrastive are two fundamental unsupervised approaches to model graph information. The graph generative models extract intra-graph information whereas the graph contrastive learning methods focus on inter-graph information. Combining these complementary sources of information can potentially enhance the expressiveness of graph representations, which, nevertheless, is underinvestigated by existing methods. In this work, we introduce a probabilistic framework called contrastive learning with edge partitioning (CLEP) that integrates generative modeling and graph contrastive learning. CLEP models edge generation by cumulative latent node interactions over multiple mutually independent hidden communities. Inspired by the ``assembly'' behavior of communities in graph generation, CEGCL learns community-specific graph embeddings and assemble them together to represent the entire graph, which are further used to predict the graph's identity via a contrastive objective. To relate each embedding to one hidden community, we define a set of community-specific weighted edges for node feature aggregation by partitioning the observed edges according to the latent node interactions associated with the corresponding hidden community. With these unique designs, CLEP is able to model the statistical dependency among hidden communities, graph structures as well as the identity of each graph; it can also be trained end-to-end via variational inference. We evaluate CLEP on real-world benchmarks under self-supervised and semi-supervised settings and achieve promising results, which demostrate the effectiveness of our method. Various exploratory studies are also conducted to highlight the characteristics of the inferred hidden communities and the potential benefits they bring to representation learning.