11 papers
Towards Fair Graph Prompting: A Dual-Prompt Mechanism for Mitigating Attribute and Structural Bias
Yuhan Yang, Xingbo Fu, Jundong Li
Self-supervised pre-training on unlabeled graph data has become a common paradigm for Graph Neural Networks (GNNs). However, an objective gap often remains between pre-training obj…
A Survey of Scaling in Large Language Model Reasoning
Zihan Chen, Song Wang, Zhen Tan +6
The rapid advancements in large Language models (LLMs) have significantly enhanced their reasoning capabilities, driven by various strategies such as multi-agent collaboration. How…
From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning
Zihan Chen, Song Wang, Xingbo Fu +4
The capability of in-context learning (ICL) enables large language models (LLMs) to perform novel tasks without parameter updates by conditioning on a few input-output examples. Ho…
GraphTOP: Graph Topology-Oriented Prompting for Graph Neural Networks
Xingbo Fu, Zhenyu Lei, Zihan Chen +3
Graph Neural Networks (GNNs) have revolutionized the field of graph learning by learning expressive graph representations from massive graph data. As a common pattern to train powe…
Text-Attributed Graph Anomaly Detection via Multi-Scale Cross- and Uni-Modal Contrastive Learning
Yiming Xu, Xu Hua, Zhen Peng +5
The widespread application of graph data in various high-risk scenarios has increased attention to graph anomaly detection (GAD). Faced with real-world graphs that often carry node…
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions
Xingbo Fu, Zehong Wang, Zihan Chen +7
Graph learning models have demonstrated great prowess in learning expressive representations from large-scale graph data in a wide variety of real-world scenarios. As a prevalent s…