activity
20222025
most citedA Survey of Graph Prompting Methods: Techniques, Applications, and Challenges

9 citations · 19 across the 11 of their papers we have counts for

collaborators

9 papers

cs.LG20242 cited

Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models

Yili Wang, Kaixiong Zhou, Ninghao Liu +2

Sharpness-aware minimization (SAM) has received increasing attention in computer vision since it can effectively eliminate the sharp local minima from the training trajectory and m…

cs.LG2024

Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models

Xu Shen, Yili Wang, Kaixiong Zhou +2

The open-world test dataset is often mixed with out-of-distribution (OOD) samples, where the deployed models will struggle to make accurate predictions. Traditional detection metho…

cs.LG2023

Marginal Nodes Matter: Towards Structure Fairness in Graphs

Xiaotian Han, Kaixiong Zhou, Ting-Hsiang Wang +3

In social network, a person located at the periphery region (marginal node) is likely to be treated unfairly when compared with the persons at the center. While existing fairness w…

cs.IR20231 cited

DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research

Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang +8

The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are…

cs.IR20231 cited

Hessian-aware Quantized Node Embeddings for Recommendation

Huiyuan Chen, Kaixiong Zhou, Kwei-Herng Lai +4

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in recommender systems. Nevertheless, the process of searching and ranking from a large item corpus usually…

cs.LG20231 cited

Editable Graph Neural Network for Node Classifications

Zirui Liu, Zhimeng Jiang, Shaochen Zhong +5

Despite Graph Neural Networks (GNNs) have achieved prominent success in many graph-based learning problem, such as credit risk assessment in financial networks and fake news detect…