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cs.LG2026
When Can You Correct Distribution Drift in Temporal Graph Generation? A Sharpening--Drift Tension and an Impossibility for Observation-Based Correction
Tianpeng Li, Xuan Guo, Wenjun Wang +2
Generative models of temporal graphs are trained on one stretch of an evolving network and deployed on the next, and they degrade badly in the gap. We show this degradation is deri…
cs.LG2024
Graphs Generalization under Distribution Shifts
Qin Tian, Wenjun Wang, Chen Zhao +3
Traditional machine learning methods heavily rely on the independent and identically distribution assumption, which imposes limitations when the test distribution deviates from the…