Toward General Digraph Contrastive Learning: A Dual Spatial Perspective

Abstract--Graph Contrastive Learning (GCL) has emerged as a powerful tool for extracting consistent representations from graphs, independent of labeled information. However, existing methods predominantly focus on undirected graphs, disregarding the pivotal directional information that is fundamental and indispensable in real-world networks (e.g., social networks and recommendations). In this paper, we introduce S2-DiGCL, a novel framework that emphasizes spatial insights from complex and real domain perspectives for directed graph (digraph) contrastive learning. From the complex-domain perspective, S2-DiGCL introduces personalized perturbations into the magnetic Laplacian to adaptively modulate edge phases and directional semantics. From the real-domain perspective, it employs a path-based subgraph augmentation strategy to capture fine-grained local asymmetries and topological dependencies. Extensive experiments on 7 real-world digraph datasets demonstrate the superiority of our approach, achieving SOT A performance with 4.41% improvement in node classification and 4.34% in link prediction under both supervised and unsupervised settings. Graph has become a fundamental data structure for modeling pairwise relationships across diverse domains, such as social interactions [1], [2], transportation networks [3], [4], and recommendation systems [5], [6]. This widespread use has spurred the rapid development of GNNs [7], [8], which effectively capture topological dependencies and node interactions. Despite advancements, conventional supervised GNNs face inherent limitations due to their reliance on extensive labeled data, posing a critical bottleneck as the volume of real-world graphs continues to grow while annotated data remain scarce and expensive to obtain. To mitigate this limitation, GCL [9], [10] has emerged as a promising self-supervised paradigm that learns robust and transferable node representations by enforcing consistency across multiple augmented graph views. While current GCL methodologies have demonstrated remarkable success on undirected graphs, their applicability to digraphs remains largely unexplored.