When Contrastive Learning Meets Graph Unlearning: Graph Contrastive Unlearning for Link Prediction

In today's data-rich era, large models continuously consume vast troves of personal data, raising pertinent questions about user consent and its implications in training machine learning frameworks. With mounting calls for enhanced user privacy, the 'right to be forgotten' is gaining traction in contemporary research. Parallelly, the ubiquity of graph neural networks underscores its ever-growing imprint on artificial intelligence communities, resonating in almost every facet of various applications. This study delves into enhancing the effectiveness of graph unlearning through the incorporation of graph contrastive learning. To adeptly erase edge information without compromising the performance of the subsequent link prediction task. We present a novel graph unlearning framework, Graph Contrastive Unlearning (GCU). Infusing the principles of contrastive learning into conventional graph unlearning paradigms, the GCU stands out, ensuring the absolute erasure of deleted edge data. The genius of graph contrastive unlearning lies in its dual capability: facilitating the model in discerning the deleted edges from their original counterparts while seamlessly aligning the neighbors of these edges with the original graph's node features. Crafted meticulously, the GCU extirpates the influence of the removed elements from model parameters and neighboring representations. Yet, it ensures the sanctity and retention of the residual model knowledge post-deletion. Empirical tests underscore the GCU's promising edge unlearning ability, revealing marked improvements in link prediction across diverse graph datasets. This not only fortifies user privacy but also streamlines the unlearning process, eliminating the cumbersome need for full model retraining even in scenarios of mass edge deletions.