Multiview Contrastive for Graph Diffusion

Graph diffusion models have recently emerged as powerful generative models for structured data. They learn data distributions by gradually transforming noise into samples through a reverse diffusion process, where the training objective can be interpreted as minimizing the discrepancy between generated and target distributions. From an information-theoretic perspective, we revisit the distribution matching objective and reveal that the entropy term is an intrinsic component of this objective. This observation indicates that preserving the information content carried by the data distribution can provide a complementary perspective for improving generative modeling. Furthermore, we show that maximizing mutual information between multiple views provides a tractable surrogate for entropy maximization. Based on this insight, we propose a multiview contrastive graph diffusion learning (MCGD) method, a framework that introduces entropy-oriented contrastive regularization into graph diffusion models. MCGD constructs stochastic views from denoised graph predictions and performs hierarchical contrastive regularization from two complementary perspectives: Graph-level contrastive learning, which enforces consistency in the prediction space, and Semantic-level contrastive learning, which aligns high-level graph representations in a latent semantic space. By jointly preserving structural information and high-order graph semantics, MCGD enables diffusion models to better capture complex graph distributions. Extensive experiments on general and molecular graph generation benchmarks demonstrate that MCGD improves generation quality and structural fidelity consistently.