Rethinking Propagation for Unsupervised Graph Domain Adaptation

Rethinking Propagation for Unsupervised Graph Domain Adaptation

8 Feb 2024 | Meihan Liu, Zeyu Fang, Zhen Zhang, Ming Gu, Sheng Zhou, Xin Wang, Jiajun Bu
The paper "Rethinking Propagation for Unsupervised Graph Domain Adaptation" by Meihan Liu, Zeyu Fang, Zhen Zhang, Ming Gu, Sheng Zhou, Xin Wang, and Jiajun Bu explores the role of Graph Neural Networks (GNNs) in unsupervised graph domain adaptation (UGDA). The authors argue that while previous works have focused on aligning data from source and target graphs in the representation space learned by GNNs, the inherent generalization capability of GNNs has been overlooked. Through empirical and theoretical analysis, they uncover the pivotal role of the propagation process in GNNs for adapting to different graph domains. The key findings include: 1. **Empirical Analysis**: Propagation operations play a crucial role in enhancing the generalization capability of GNNs. Specifically, increasing the number of propagation layers can significantly improve performance, while adding transformation layers can impair it. 2. **Theoretical Analysis**: The authors derive a generalization bound for multi-layer GNNs and show that the target risk bound can be tighter by removing propagation layers from the source graph and stacking multiple propagation layers on the target graph. 3. **Proposed Framework (A2GNN)**: Based on these findings, they propose A2GNN, an asymmetric GNN architecture that uses a single transformation layer on the source graph and multiple propagation layers on the target graph. This approach is shown to achieve superior performance on various real-world datasets. The paper also includes extensive experiments on real-world datasets, demonstrating the effectiveness of A2GNN in node classification tasks. The results highlight that A2GNN outperforms state-of-the-art baselines, both in terms of accuracy and robustness to domain shifts. The authors conclude by discussing the implications of their findings and future directions, including extending the model to more complex scenarios such as source-free and open-set graph domain adaptation.The paper "Rethinking Propagation for Unsupervised Graph Domain Adaptation" by Meihan Liu, Zeyu Fang, Zhen Zhang, Ming Gu, Sheng Zhou, Xin Wang, and Jiajun Bu explores the role of Graph Neural Networks (GNNs) in unsupervised graph domain adaptation (UGDA). The authors argue that while previous works have focused on aligning data from source and target graphs in the representation space learned by GNNs, the inherent generalization capability of GNNs has been overlooked. Through empirical and theoretical analysis, they uncover the pivotal role of the propagation process in GNNs for adapting to different graph domains. The key findings include: 1. **Empirical Analysis**: Propagation operations play a crucial role in enhancing the generalization capability of GNNs. Specifically, increasing the number of propagation layers can significantly improve performance, while adding transformation layers can impair it. 2. **Theoretical Analysis**: The authors derive a generalization bound for multi-layer GNNs and show that the target risk bound can be tighter by removing propagation layers from the source graph and stacking multiple propagation layers on the target graph. 3. **Proposed Framework (A2GNN)**: Based on these findings, they propose A2GNN, an asymmetric GNN architecture that uses a single transformation layer on the source graph and multiple propagation layers on the target graph. This approach is shown to achieve superior performance on various real-world datasets. The paper also includes extensive experiments on real-world datasets, demonstrating the effectiveness of A2GNN in node classification tasks. The results highlight that A2GNN outperforms state-of-the-art baselines, both in terms of accuracy and robustness to domain shifts. The authors conclude by discussing the implications of their findings and future directions, including extending the model to more complex scenarios such as source-free and open-set graph domain adaptation.
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