3 citations · 3 across the 8 of their papers we have counts for
5 papers · 1 filter
GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback
Ruiyao Xu, Kaize Ding
Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic understanding ability on textual node features. However,…
A Survey of Large Language Models for Text-Guided Molecular Discovery: from Molecule Generation to Optimization
Ziqing Wang, Kexin Zhang, Zihan Zhao +4
Large language models (LLMs) are introducing a paradigm shift in molecular discovery by enabling text-guided interaction with chemical spaces through natural language, symbolic not…
A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation
Kexin Zhang, Shuhan Liu, Song Wang +6
Distribution shifts on graphs -- the discrepancies in data distribution between training and employing a graph machine learning model -- are ubiquitous and often unavoidable in rea…
Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey
Ruiyao Xu, Kaize Ding
Detecting anomalies or out-of-distribution (OOD) samples is critical for maintaining the reliability and trustworthiness of machine learning systems. Recently, Large Language Model…
Beyond Generalization: A Survey of Out-Of-Distribution Adaptation on Graphs
Shuhan Liu, Kaize Ding
Distribution shifts on graphs -- the data distribution discrepancies between training and testing a graph machine learning model, are often ubiquitous and unavoidable in real-world…