On the Benefits of Attribute-Driven Graph Domain Adaptation
Ruiyi Fang, Bingheng Li, Zhao Kang, Qiuhao Zeng, Nima Hosseini, Dashtbayaz, Ruizhi Pu, Boyu Wang, Charles Ling
TL;DR
This paper highlights the importance of node attribute alignment in Graph Domain Adaptation, demonstrating that attribute shifts are more significant than topology shifts and proposing a new method to improve domain adaptation by aligning attributes.
Contribution
The paper introduces a novel approach that emphasizes attribute-driven alignment in GDA, supported by theoretical proof and empirical evidence, improving cross-network learning.
Findings
Node attribute discrepancy significantly impacts GDA.
Attribute shift exceeds topology shift in magnitude.
Proposed method improves performance on benchmark datasets.
Abstract
Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets. Recent studies attempted to learn domain invariant representations by eliminating structural shifts between graphs. In this work, we show that existing methodologies have overlooked the significance of the graph node attribute, a pivotal factor for graph domain alignment. Specifically, we first reveal the impact of node attributes for GDA by theoretically proving that in addition to the graph structural divergence between the domains, the node attribute discrepancy also plays a critical role in GDA. Moreover, we also empirically show that the attribute shift is more substantial than the topology shift, which further underscores the importance of node attribute alignment in GDA. Inspired by this finding, a novel…
Peer Reviews
Decision·ICLR 2025 Poster
- Unlike previous methods that primarily focused on graph structural information, this approach provides a novel perspective by emphasizing the importance of node attributes in GDA. - A solid theoretical analysis proved that domain discrepancy comes from both graph structure and node attributes. - Strong experimental results.
- The motivation for using an attention-like strategy is unclear. - L_A contains both $||att^S - att^T||_2^2$ and $||att_f^S - att_f^T||_2^2$. There is no clear demonstration of which component is more important for performance. To highlight the effectiveness of aligning attributes, we need to check the effectiveness of $||att_f^S - att_f^T||_2^2$ (with/without Coss-view Matrix Refinement). - The effect of $L_D$ is unclear. It is necessary to verify the contribution of $L_D$ compared to $L_A$, a
1. This paper makes a good observation that node attributes are important for domain adaptation tasks and then follows up with a theoretical analysis. 2. The authors conducted comprehensive experiments to validate the algorithm's performance with a sufficient number of baseline methods compared. 3. The ablation study shows the effectiveness of the proposed method and its key components.
1. **Writing**: The writing of the paper can be improved. In particular, citation errors appear on the very first page of the paper. The author should proofread the paper more carefully before submission. Some sentences contain grammatical errors. 2. **Theoretical novelty**: The theoretical analysis of the paper is a direct application of the PAC-Bayesian results derived from [1], including the problem formulation and Theorem 1. The discrepancy between subgroups in [1] can be naturally applied t
- The studied problem is interesting and important. - The paper is well-organized and clearly written. - The experiments are extensive and helpful to validate the effectiveness of the model.
- Some citation styles are not correct, e.g., Line 37. - The paper lacks some of the latest baselines such as "Information filtering and interpolating for semi-supervised graph domain adaptation (2024)" - Masking node attributes could have the risk of losing crucial information. How about alignment in the embedding space? - I suggest that the authors should use some tSNE to show the effectiveness of the method. - The parameter sensitivity needs more analysis. How to apply your method to a new d
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Taxonomy
TopicsAdvanced Graph Neural Networks · Online Learning and Analytics · Data Stream Mining Techniques
MethodsALIGN