Non-IID Transfer Learning on Graphs


  • Jun Wu University of Illinois Urbana–Champaign
  • Jingrui He University of Illinois Urbana-Champaign
  • Elizabeth Ainsworth University of Illinois Urbana-Champaign USDA ARS Global Change and Photosynthesis Research Unit



ML: Transfer, Domain Adaptation, Multi-Task Learning, DMKM: Graph Mining, Social Network Analysis & Community Mining


Transfer learning refers to the transfer of knowledge or information from a relevant source domain to a target domain. However, most existing transfer learning theories and algorithms focus on IID tasks, where the source/target samples are assumed to be independent and identically distributed. Very little effort is devoted to theoretically studying the knowledge transferability on non-IID tasks, e.g., cross-network mining. To bridge the gap, in this paper, we propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph. The crucial idea is to characterize the cross-network knowledge transferability from the perspective of the Weisfeiler-Lehman graph isomorphism test. To this end, we propose a novel Graph Subtree Discrepancy to measure the graph distribution shift between source and target graphs. Then the generalization error bounds on cross-network transfer learning, including both cross-network node classification and link prediction tasks, can be derived in terms of the source knowledge and the Graph Subtree Discrepancy across domains. This thereby motivates us to propose a generic graph adaptive network (GRADE) to minimize the distribution shift between source and target graphs for cross-network transfer learning. Experimental results verify the effectiveness and efficiency of our GRADE framework on both cross-network node classification and cross-domain recommendation tasks.




How to Cite

Wu, J., He, J., & Ainsworth, E. (2023). Non-IID Transfer Learning on Graphs. Proceedings of the AAAI Conference on Artificial Intelligence, 37(9), 10342-10350.



AAAI Technical Track on Machine Learning IV