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cs.LG2025★ 4 cited
RegionGCN: Spatial-Heterogeneity-Aware Graph Convolutional Networks
Hao Guo, Han Wang, Di Zhu +3
Modeling spatial heterogeneity in the data generation process is essential for understanding and predicting geographical phenomena. Despite their prevalence in geospatial tasks, ne…
cs.LG2024
Bridging OOD Detection and Generalization: A Graph-Theoretic View
Han Wang, Yixuan Li
In the context of modern machine learning, models deployed in real-world scenarios often encounter diverse data shifts like covariate and semantic shifts, leading to challenges in…
cs.LG2024
Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled
Shengchao Liu, Chengpeng Wang, Jiarui Lu +5
Deep generative models (DGMs) have been widely developed for graph data. However, much less investigation has been carried out on understanding the latent space of such pretrained…