Non-IID Transfer Learning on Graphs

• URL: http://arxiv.org/abs/2212.08174v1
• Date: Thu, 15 Dec 2022 22:29:29 GMT
• Title: Non-IID Transfer Learning on Graphs
• Authors: Jun Wu, Jingrui He, Elizabeth Ainsworth
• Abstract summary: Transfer learning refers to the transfer of knowledge from a relevant source domain to a target domain. We propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph.
• Score: 35.84135001172101
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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