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Groups Don't Move as One: A Bregman Clustering View of Linearized Group Influence
Hyunho Choi, Jaeseung Heo, Youngbin Choi +2
Influence functions provide an efficient alternative to retraining for assessing the effects of removing or reweighting training data. For groups, however, the standard first-order…
The Oversmoothing Fallacy: A Misguided Narrative in GNN Research
MoonJeong Park, Sunghyun Choi, Jaeseung Heo +2
Oversmoothing has been recognized as a main obstacle to building deep Graph Neural Networks (GNNs), limiting the performance. This position paper argues that the influence of overs…
Influence Functions for Edge Edits in Non-Convex Graph Neural Networks
Jaeseung Heo, Kyeongheung Yun, Seokwon Yoon +3
Understanding how individual edges influence the behavior of graph neural networks (GNNs) is essential for improving their interpretability and robustness. Graph influence function…
Taming Gradient Oversmoothing and Expansion in Graph Neural Networks
MoonJeong Park, Dongwoo Kim
Oversmoothing has been claimed as a primary bottleneck for multi-layered graph neural networks (GNNs). Multiple analyses have examined how and why oversmoothing occurs. However, no…
Posterior Label Smoothing for Node Classification
Jaeseung Heo, Moonjeong Park, Dongwoo Kim
Label smoothing is a widely studied regularization technique in machine learning. However, its potential for node classification in graph-structured data, spanning homophilic to he…