Uncertainty-Aware Fuzzy Graph Contrastive Learning

Graph Contrastive Learning (GCL) is an effective approach for learning node representations. However, the intrinsic uncertainty of real-world graph data introduces significant challenges, as GCL relies on crisp deterministic representations that struggle to capture such ambiguity. To model graph uncertainty, fuzzy logic has attracted increasing attention due to its mathematical rigor. Existing methods, however, simply inject fuzzy modeling into the contrastive framework without tailoring data augmentation to the needs of fuzzy representations, resulting in limited discriminability and suboptimal downstream performance. To address these limitations, we propose a novel Fuzzy Graph Contrastive Learning (FGCL) framework for uncertainty-aware node representation learning. It adopts a vertex entanglement-based augmentation strategy to generate tailored multi-views with controlled perturbations while preserving core graph topology and semantics, mitigating semantic corruption. We also design a Deep Weighted Fuzzy Graph Convolutional Neural Network (DWFGCNN) encoder, which maps crisp features to fuzzy representations via learnable membership functions, explicitly models uncertainty, and resolves the core contradiction that traditional encoders cannot balance discriminability and robustness simultaneously. Extensive experiments on multiple datasets demonstrate that FGCL consistently outperforms baselines in node classification, community detection, and link prediction tasks, validating its effectiveness in handling uncertainty in graph representation learning.