Adaptive Multi-view Graph Contrastive Learning via Fractional Continuous Dynamics

Graph contrastive learning (GCL) learns node and graph representations by contrasting multiple views of the same graph. Existing methods predominantly rely on a small set of fixed, handcrafted views—typically a local and a global perspective—which fundamentally constrains their capacity to capture multi-scale structural patterns. We present an augmentation-free, multi-view GCL framework grounded in fractional-order continuous dynamics. By systematically varying the fractional derivative order $\alpha \in (0,1]$, our encoders produce a continuous spectrum of semantically distinct views: small values of $\alpha$ induce localized, memory-attenuated feature propagation, whereas values approaching $1$ recover broader, global aggregation. Crucially, we treat $\alpha$ as a learnable parameter, enabling the model to automatically identify informative views in a data-driven manner, without resorting to manual view engineering. Extensive experiments on standard node- and graph-level benchmarks demonstrate that the resulting representations are more robust and expressive, consistently outperforming state-of-the-art GCL baselines.