Multi- View Teacher with Curriculum Data Fusion for Robust Unsupervised Domain Adaptation

Graph Neural Networks (GNNs) have emerged as an effective tool for graph classification, yet their reliance on extensive labeled data poses a significant challenge, especially when such labels are scarce. To address this challenge, this paper presents a novel framework, denoted as Multi-View Teacher with Curriculum Data Fusion (MTDF). MTDF achieves robust unsupervised domain adaptation in both the model and data perspectives. On the one hand, MTDF utilizes a multi-teacher framework with diverse update strategies for robust adaptation. Moreover, it employs a complementary perspective consistency model from local implicit representation and global explicit graph structure. On the other hand, MTDF generates source-mimicry data at the target domain to serve as a bridge to overcome the challenge of domain shift. MTDF achieves stable unsupervised domain adaptation through bi-directional processes from the perspective of both the model and the data. We have conducted comprehensive experimental evaluations across multiple real-world datasets with a range of baseline methods to demonstrate the superior performance of our proposed method.