Summary of Smoothness Really Matters: a Simple Yet Effective Approach For Unsupervised Graph Domain Adaptation, by Wei Chen et al.
Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation
by Wei Chen, Guo Ye, Yakun Wang, Zhao Zhang, Libang Zhang, Daixin Wang, Zhiqiang Zhang, Fuzhen Zhuang
First submitted to arxiv on: 16 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Target-Domain Structural Smoothing (TDSS), a novel approach for unsupervised graph domain adaptation. TDSS aims to bridge distribution shifts between domains by transferring knowledge from labeled source graphs to given unlabeled target graphs, while addressing structural complexity and reducing the risk of over-smoothing. The method integrates smoothing techniques with neighborhood sampling to maintain structural coherence in the target graph. Experimental results on three real-world datasets demonstrate significant improvements across six transfer scenarios, outperforming recent state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TDSS is a new way to help machines learn from different types of data without labeled information. It tries to match the structure of one dataset with another, so they can share knowledge. The method works by smoothing out small differences in the structure of the target graph, making it easier for machines to understand. This helps improve how well models transfer learning between datasets. The results show that TDSS does a better job than other methods at adapting to different types of data. |
Keywords
» Artificial intelligence » Domain adaptation » Transfer learning » Unsupervised
