Continual Graph

Continual graph learning (CGL) addresses the challenge of adapting graph neural networks (GNNs) to continuously evolving graph data, preventing catastrophic forgetting of previously learned knowledge. Current research focuses on developing efficient algorithms, such as experience replay and graph condensation techniques, to manage the growing graph size and maintain performance across multiple tasks. These methods often incorporate strategies to leverage both node features and graph topology for improved knowledge retention and generalization. CGL's significance lies in its potential to enable more robust and adaptable machine learning models for applications involving dynamic graph data, such as recommendation systems and social network analysis.