MIRAGE: Hierarchical MI-Surrogate Regulation for Graph Contrastive Learning

Graph contrastive learning (GCL) typically maximizes cross-view agreement via an InfoNCE-based mutual-information (MI) surrogate, applying uniform alignment pressure across all anchors. Yet the reliability with which topology and attributes support cross-view agreement varies across nodes: strong pressure can benefit structurally consistent anchors but can harm boundary, low-agreement, or noisy anchors. We study this calibration problem in self-supervised node representation learning on small-to-medium attributed graphs. We propose MIRAGE, a hierarchical MI-surrogate regulation framework whose core consists of two components: anchor-wise MI setpoints that assign bounded alignment budgets to individual nodes, and dual-view MI stabilization that keeps branch-level surrogate estimates close to a target while limiting excessive anchor-wise dispersion. A label-free structure-reliability-gated hypergraph path serves only as conditional compensation for weak anchors when local structural signals are estimated to be reliable. On six node-classification benchmarks, MIRAGE achieves competitive accuracy, including 85.21% on Cora, 73.94% on Citeseer, and 92.57% on ACM. Beyond final accuracy, mechanism-level analyses show that MIRAGE tracks designated MI-surrogate targets during training and that changing the target level produces measurable downstream changes, indicating that target-tracked MI-surrogate regulation provides a practical optimization primitive for calibrating cross-view agreement in GCL.