Graph Unlearning

Graph unlearning focuses on efficiently removing the influence of specific data points (nodes, edges, or features) from a trained graph neural network (GNN) model, addressing privacy concerns and data obsolescence. Current research emphasizes developing efficient algorithms that avoid retraining the entire model, exploring techniques like parameter editing, subgraph manipulation, and knowledge distillation to achieve this. These advancements are crucial for responsible AI development, enabling compliance with data protection regulations and improving the robustness and trustworthiness of GNNs in various applications, including social networks and financial systems.