30 citations · 50 across the 12 of their papers we have counts for
7 papers · 1 filter
Non-autoregressive Personalized Bundle Generation
Wenchuan Yang, Cheng Yang, Jichao Li +3
The personalized bundle generation problem, which aims to create a preferred bundle for user from numerous candidate items, receives increasing attention in recommendation. However…
FairSIN: Achieving Fairness in Graph Neural Networks through Sensitive Information Neutralization
Cheng Yang, Jixi Liu, Yunhe Yan +1
Despite the remarkable success of graph neural networks (GNNs) in modeling graph-structured data, like other machine learning models, GNNs are also susceptible to making biased pre…
Endowing Pre-trained Graph Models with Provable Fairness
Zhongjian Zhang, Mengmei Zhang, Yue Yu +3
Pre-trained graph models (PGMs) aim to capture transferable inherent structural properties and apply them to different downstream tasks. Similar to pre-trained language models, PGM…
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution Generalization
Tianrui Jia, Haoyang Li, Cheng Yang +2
Graph neural networks (GNNs) have been demonstrated to perform well in graph representation learning, but always lacking in generalization capability when tackling out-of-distribut…
Graph Foundation Models: Concepts, Opportunities and Challenges
Jiawei Liu, Cheng Yang, Zhiyuan Lu +8
Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and seve…
Graph Distillation with Eigenbasis Matching
Yang Liu, Deyu Bo, Chuan Shi
The increasing amount of graph data places requirements on the efficient training of graph neural networks (GNNs). The emerging graph distillation (GD) tackles this challenge by di…
Data-centric Graph Learning: A Survey
Yuxin Guo, Deyu Bo, Cheng Yang +5
The history of artificial intelligence (AI) has witnessed the significant impact of high-quality data on various deep learning models, such as ImageNet for AlexNet and ResNet. Rece…