83 citations · 203 across the 18 of their papers we have counts for
5 papers · 1 filter
Large Language Models on Graphs: A Comprehensive Survey
Bowen Jin, Gang Liu, Chi Han +3
Large language models (LLMs), such as GPT4 and LLaMA, are creating significant advancements in natural language processing, due to their strong text encoding/decoding ability and n…
Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection
Noah Ziems, Gang Liu, John Flanagan +1
Network intrusion detection (NID) systems which leverage machine learning have been shown to have strong performance in practice when used to detect malicious network traffic. Deci…
Motif-aware Attribute Masking for Molecular Graph Pre-training
Eric Inae, Gang Liu, Meng Jiang
Attribute reconstruction is used to predict node or edge features in the pre-training of graph neural networks. Given a large number of molecules, they learn to capture structural…
Semi-Supervised Graph Imbalanced Regression
Gang Liu, Tong Zhao, Eric Inae +2
Data imbalance is easily found in annotated data when the observations of certain continuous label values are difficult to collect for regression tasks. When they come to molecule…
Data-Centric Learning from Unlabeled Graphs with Diffusion Model
Gang Liu, Eric Inae, Tong Zhao +3
Graph property prediction tasks are important and numerous. While each task offers a small size of labeled examples, unlabeled graphs have been collected from various sources and a…