Graph Domain Adaptation

Graph Domain Adaptation is a cross-domain graph data transfer learning method aimed at leveraging the graph structure and label information from the source domain to enhance the predictive performance of graph data in the target domain. The core objective of this technique is to address the distribution differences between different graph data domains, thereby achieving effective knowledge transfer. Its application value lies in enhancing the generalization ability of graph data in new environments, improving the adaptability and robustness of models, and it is widely applicable in areas such as social network analysis, bioinformatics, recommendation systems, etc.