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cs.LG2026
WorldGraph: Graph-Native World Modeling
Zezhong Ding, Yipeng Li, Xike Xie
World models infer latent states of an environment to capture its underlying dynamics and predict future evolution. Many real-world environments, however, are inherently relational…
cs.LG2026
Gaussian Relational Graph Transformer
Zezhong Ding, Jin Li, Xugang Wang +1
Relational graph learning enables predictive modeling over relational databases by directly capturing dependencies across interconnected tables. While relational graph Transformers…
cs.LG2026
Learning Graph Foundation Models on Riemannian Graph-of-Graphs
Haokun Liu, Zezhong Ding, Xike Xie
Graph foundation models (GFMs), pretrained on massive graph data, have transformed graph machine learning by supporting general-purpose reasoning across diverse graph tasks and dom…