45 citations · 73 across the 10 of their papers we have counts for
10 papers
ProG: A Graph Prompt Learning Benchmark
Chenyi Zi, Haihong Zhao, Xiangguo Sun +3
Artificial general intelligence on graphs has shown significant advancements across various applications, yet the traditional 'Pre-train & Fine-tune' paradigm faces inefficiencies…
All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)
Xiangguo Sun, Hong Cheng, Jia Li +2
This paper is an extended abstract of our original work published in KDD23, where we won the best research paper award (Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan. A…
Protein Multimer Structure Prediction via Prompt Learning
Ziqi Gao, Xiangguo Sun, Zijing Liu +3
Understanding the 3D structures of protein multimers is crucial, as they play a vital role in regulating various cellular processes. It has been empirically confirmed that the mult…
All in One and One for All: A Simple yet Effective Method towards Cross-domain Graph Pretraining
Haihong Zhao, Aochuan Chen, Xiangguo Sun +2
Large Language Models (LLMs) have revolutionized the fields of computer vision (CV) and natural language processing (NLP). One of the most notable advancements of LLMs is that a si…
Graph Prompt Learning: A Comprehensive Survey and Beyond
Xiangguo Sun, Jiawen Zhang, Xixi Wu +3
Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration with graph data, a cornerstone in our interconnected world, remains nascent. This pape…
A Survey of Graph Meets Large Language Model: Progress and Future Directions
Yuhan Li, Zhixun Li, Peisong Wang +4
Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. Recen…
All in One: Multi-task Prompting for Graph Neural Networks
Xiangguo Sun, Hong Cheng, Jia Li +2
Recently, ''pre-training and fine-tuning'' has been adopted as a standard workflow for many graph tasks since it can take general graph knowledge to relieve the lack of graph annot…
Decision Support System for Chronic Diseases Based on Drug-Drug Interactions
Tian Bian, Yuli Jiang, Jia Li +6
Many patients with chronic diseases resort to multiple medications to relieve various symptoms, which raises concerns about the safety of multiple medication use, as severe drug-dr…
Self-supervised Hypergraph Representation Learning for Sociological Analysis
Xiangguo Sun, Hong Cheng, Bo Liu +4
Modern sociology has profoundly uncovered many convincing social criteria for behavioural analysis. Unfortunately, many of them are too subjective to be measured and presented in o…
Deconvolutional Networks on Graph Data
Jia Li, Jiajin Li, Yang Liu +3
In this paper, we consider an inverse problem in graph learning domain -- ``given the graph representations smoothed by Graph Convolutional Network (GCN), how can we reconstruct th…