4 papers · 1 filter
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…
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…
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…
SamGoG: A Sampling-Based Graph-of-Graphs Framework for Imbalanced Graph Classification
Shangyou Wang, Zezhong Ding, Xike Xie
Graph Neural Networks (GNNs) have shown remarkable success in graph classification tasks by capturing both structural and feature-based representations. However, real-world graphs…