45 citations · 78 across the 15 of their papers we have counts for
10 papers · 1 filter
Efficient Multi-modal Large Language Models via Visual Token Grouping
Minbin Huang, Runhui Huang, Han Shi +6
The development of Multi-modal Large Language Models (MLLMs) enhances Large Language Models (LLMs) with the ability to perceive data formats beyond text, significantly advancing a…
Adaptive Graph Integration for Cross-Domain Recommendation via Heterogeneous Graph Coordinators
Hengyu Zhang, Chunxu Shen, Xiangguo Sun +5
In the digital era, users typically interact with diverse items across multiple domains (e.g., e-commerce, streaming platforms, and social networks), generating intricate heterogen…
Does Graph Prompt Work? A Data Operation Perspective with Theoretical Analysis
Qunzhong Wang, Xiangguo Sun, Hong Cheng
In recent years, graph prompting has emerged as a promising research direction, enabling the learning of additional tokens or subgraphs appended to the original graphs without requ…
When LLM Meets Hypergraph: A Sociological Analysis on Personality via Online Social Networks
Zhiyao Shu, Xiangguo Sun, Hong Cheng
Individual personalities significantly influence our perceptions, decisions, and social interactions, which is particularly crucial for gaining insights into human behavior pattern…
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…
Prompt Learning on Temporal Interaction Graphs
Xi Chen, Siwei Zhang, Yun Xiong +6
Temporal Interaction Graphs (TIGs) are widely utilized to represent real-world systems. To facilitate representation learning on TIGs, researchers have proposed a series of TIG mod…
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…
DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning
Yingying Wang, Yun Xiong, Xixi Wu +2
Drug combinations can cause adverse drug-drug interactions(DDIs). Identifying specific effects is crucial for developing safer therapies. Previous works on DDI event prediction hav…
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…