Computer Science > Machine Learning
arXiv:2603.09257 (cs)
[Submitted on 10 Mar 2026]
Title:Transductive Generalization via Optimal Transport and Its Application to Graph Node Classification
Authors:MoonJeong Park, Seungbeom Lee, Kyungmin Kim, Jaeseung Heo, Seunghyuk Cho, Shouheng Li, Sangdon Park, Dongwoo Kim
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Abstract:Many existing transductive bounds rely on classical complexity measures that are computationally intractable and often misaligned with empirical behavior. In this work, we establish new representation-based generalization bounds in a distribution-free transductive setting, where learned representations are dependent, and test features are accessible during training. We derive global and class-wise bounds via optimal transport, expressed in terms of Wasserstein distances between encoded feature distributions. We demonstrate that our bounds are efficiently computable and strongly correlate with empirical generalization in graph node classification, improving upon classical complexity measures. Additionally, our analysis reveals how the GNN aggregation process transforms the representation distributions, inducing a trade-off between intra-class concentration and inter-class separation. This yields depth-dependent characterizations that capture the non-monotonic relationship between depth and generalization error observed in practice. The code is available at this https URL.
| Subjects: | Machine Learning (cs.LG); Machine Learning (stat.ML) |
| Cite as: | arXiv:2603.09257 [cs.LG] |
| (or arXiv:2603.09257v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09257
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View a PDF of the paper titled Transductive Generalization via Optimal Transport and Its Application to Graph Node Classification, by MoonJeong Park and 7 other authors
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