Pairwise Alignment (Pair-Align) is introduced as a method to counter graph structure shift by addressing conditional structure shift (CSS) and label shift (LS). The approach involves recalibrating edge weights and adjusting classification loss with label weights. Pair-Align shows strong performance in various applications, outperforming baselines significantly.
The content discusses the challenges of distribution shifts in graph data, focusing on CSS and LS. The proposed algorithm iteratively addresses CSS using edge reweighting based on gamma estimation and handles LS through beta estimation. The effectiveness of Pair-Align is demonstrated through experiments on synthetic datasets, MAG datasets, Pileup Mitigation tasks, Arxiv, DBLP, and ACM datasets.
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arxiv.org
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