CH-GCCIL: Clique-Hierarchical Spectral Intervention for Graph Causal Contrastive Invariant Learning

Graph contrastive learning has emerged as a dominant framework for graph representation learning by learning discriminative node representations via graph augmentation. Nevertheless, conventional stochastic augmentation schemes fail to differentiate causal factors from non-causal confounding factors, making the learned representations prone to capturing spurious correlational patterns rather than invariant causal structures. Current causal-invariant graph learning methods primarily rely on global spectral decomposition, which imposes a uniform frequency partition of causal and non-causal components on the entire graph. Such global paradigms overlook the inherent structural heterogeneity of graph cliques: nodes inside individual cliques exhibit consistent causal mechanisms, while cross-clique connections are dominated by non-causal noisy associations, thereby resulting in coarse and inaccurate causal separation. To address this limitation, this paper proposes a novel Graph Causal Contrastive Invariant Learning method with Clique-Hierarchical Spectral Intervention (CH-GCCIL) to achieve fine-grained causal disentanglement from a clique hierarchical perspective. Specifically, we first mine maximal cliques from the graph and categorize them into strongly causal cliques and noisy cliques based on a three-dimensional confidence metric. On this basis, we construct asymmetric dual augmented views and adopt Chebyshev approximation for efficient spectral filtering. Furthermore, we design a dual-scale contrastive loss function to optimize representation learning at both global graph and intra-clique levels. Extensive node classification experiments on five benchmark datasets validate the superiority of CH-GCCIL. It achieves Macro-F1 scores of 78.8%, 84.1%, and 44.1% on Wiki-CS, PubMed, and Flickr datasets, outperforming state-of-the-art baseline methods by 3.2%, 2.6%, and 4.1% respectively, which demonstrates the effectiveness and advancement of the proposed clique-hierarchical spectral intervention mechanism.