Rethinking Graph Contrastive Learning through Relative Similarity Preservation
Graph contrastive learning (GCL) has achieved remarkable success by following the computer vision paradigm of preserving absolute similarity between augmented views. However, this approach faces fundamental challenges in graphs due to their discrete, non-Euclidean nature -- view generation often breaks semantic validity and similarity verification becomes unreliable. Through analyzing 11 real-world graphs, we discover a universal pattern transcending the homophily-heterophily dichotomy: label consistency systematically diminishes as structural distance increases, manifesting as smooth decay in homophily graphs and oscillatory decay in heterophily graphs. We establish theoretical guarantees for this pattern through random walk theory, proving label distribution convergence and characterizing the mechanisms behind different decay behaviors. This discovery reveals that graphs naturally encode relative similarity patterns, where structurally closer nodes exhibit collectively stronger semantic relationships. Leveraging this insight, we propose RELGCL, a novel GCL framework with complementary pairwise and listwise implementations that preserve these inherent patterns through collective similarity objectives. Extensive experiments demonstrate that our method consistently outperforms 20 existing approaches across both homophily and heterophily graphs, validating the effectiveness of leveraging natural relative similarity over artificial absolute similarity.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Scene Graph Embeddings Using Relative Similarity Supervision
Scene graphs are a powerful structured representation of the underlying content of images, and embeddings derived from them have been shown to be useful in multiple downstream tasks. In this work, we employ a graph convo…
Contrastive LearningImage RetrievalRetrievalRethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective
Graph anomaly detection aims to identify unusual patterns in graph-based data, with wide applications in fields such as web security and financial fraud detection. Existing methods typically rely on contrastive learning,…
Anomaly DetectionContrastive LearningFraud DetectionGraph Anomaly DetectionRethinking Weak Supervision in Helping Contrastive Learning
Contrastive learning has shown outstanding performances in both supervised and unsupervised learning, and has recently been introduced to solve weakly supervised learning problems such as semi-supervised learning and noi…
Contrastive LearningDenoisingWeakly-supervised LearningSymmetric Graph Contrastive Learning against Noisy Views for Recommendation
Graph Contrastive Learning (GCL) leverages data augmentation techniques to produce contrasting views, enhancing the accuracy of recommendation systems through learning the consistency between contrastive views. However, …
Contrastive LearningData AugmentationRecommendation SystemsHow does Contrastive Learning Organize Images?
Contrastive learning, a dominant self-supervised technique, emphasizes similarity in representations between augmentations of the same input and dissimilarity for different ones. Although low contrastive loss often corre…
ClusteringContrastive Learning