3 papers
cs.LG2025
Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness-Generalization Perspective
Ming Gu, Zhuonan Zheng, Sheng Zhou +5
Graph Neural Networks (GNNs) have achieved great success but are often considered to be challenged by varying levels of homophily in graphs. Recent \textit{empirical} studies have…
cs.LG2025
OpenGT: A Comprehensive Benchmark For Graph Transformers
Jiachen Tang, Zhonghao Wang, Sirui Chen +3
Graph Transformers (GTs) have recently demonstrated remarkable performance across diverse domains. By leveraging attention mechanisms, GTs are capable of modeling long-range depend…
cs.IR2024
Better Late Than Never: Formulating and Benchmarking Recommendation Editing
Chengyu Lai, Sheng Zhou, Zhimeng Jiang +5
Recommendation systems play a pivotal role in suggesting items to users based on their preferences. However, in online platforms, these systems inevitably offer unsuitable recommen…