Jingzhe Liu — accepted AI-conference papers

2026

Learning the Latent Structure: A Feature-Centric Approach to Graph Data Augmentation

Yu Song, Zhigang Hua, Yan Xie, Bingheng Li, Jingzhe Liu, Bo Long +2

AAAI 2026technical

Graph-structured data plays a pivotal role in modeling complex relationships. However, real-world graphs are often incomplete due to data collection and observational constraints, severely limiting the effectiveness of modern graph learning pipelines. While existing Graph Data Augmentation (GDA) met

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2024

Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights

Zhikai Chen, Haitao Mao, Jingzhe Liu, Yu Song, Bingheng Li, Wei Jin +5

NeurIPS 2024poster

Given the ubiquity of graph data and its applications in diverse domains, building a Graph Foundation Model (GFM) that can work well across different graphs and tasks with a unified backbone has recently garnered significant interests. A major obstacle to achieving this goal stems from the fact that…

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