Adapting Graph Models via Target Integrity Assessment and Source Distribution Hypothesis.

Abstract

This article addresses the challenge of domain adaptation on graphs, a specialized form of graph transfer learning (GTL), which involves adapting a graph model trained on source graphs to unlabeled target graphs that significantly differ in distribution. Traditional methods often rely heavily on the source graph to transfer learned task knowledge, but certain situations may render the source graph unavailable or restricted due to privacy or security concerns, thus impeding the usability and flexibility of graph model adaptation. Therefore, this article studies the problem of source-free domain adaptation (SFDA) in graph transfer learning (GTL). Our objective is to adapt a pretrained model to effectively operate on the target graph without the need to access the source graph. To achieve this, we first incorporate a weighted information maximization loss to enhance the model's discriminative ability on the target graph, where we introduce the concept of posterior integrities of target nodes to assess their optimization confidence. Then, we estimate the distributions of the source graph and generate synthesized source nodes. We propose a reconstruction decoder to enhance the authenticity of the synthesized nodes and use adversarial learning to align the distributions between graphs, leading to improved adaptation of the model. Finally, extensive experimental results on a range of publicly accessible datasets demonstrate the superior performance of our method over the state of the art.