2 papers
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.LG2026
Unsupervised Graph Representation Learning with Complementary View Alignment
Zengyi Wo, Shiyu Zhang, Qiyao Peng +2
Unsupervised graph representation learning aims to derive meaningful node embeddings by capturing both structural and attribute information without relying on labeled data. Existin…