215 citations · 498 across the 15 of their papers we have counts for
6 papers · 1 filter
EasyST: A Simple Framework for Spatio-Temporal Prediction
Jiabin Tang, Wei Wei, Lianghao Xia +1
Spatio-temporal prediction is a crucial research area in data-driven urban computing, with implications for transportation, public safety, and environmental monitoring. However, sc…
AnyGraph: Graph Foundation Model in the Wild
Lianghao Xia, Chao Huang
The growing ubiquity of relational data structured as graphs has underscored the need for graph learning models with exceptional generalization capabilities. However, current appro…
OpenCity: Open Spatio-Temporal Foundation Models for Traffic Prediction
Zhonghang Li, Long Xia, Lei Shi +3
Accurate traffic forecasting is crucial for effective urban planning and transportation management, enabling efficient resource allocation and enhanced travel experiences. However,…
A Survey of Large Language Models for Graphs
Xubin Ren, Jiabin Tang, Dawei Yin +2
Graphs are an essential data structure utilized to represent relationships in real-world scenarios. Prior research has established that Graph Neural Networks (GNNs) deliver impress…
OpenGraph: Towards Open Graph Foundation Models
Lianghao Xia, Ben Kao, Chao Huang
Graph learning has become essential in various domains, including recommendation systems and social network analysis. Graph Neural Networks (GNNs) have emerged as promising techniq…
GraphEdit: Large Language Models for Graph Structure Learning
Zirui Guo, Lianghao Xia, Yanhua Yu +4
Graph Structure Learning (GSL) focuses on capturing intrinsic dependencies and interactions among nodes in graph-structured data by generating novel graph structures. Graph Neural…