30 citations · 31 across the 7 of their papers we have counts for
7 papers
CORE: Contrastive Masked Feature Reconstruction on Graphs
Jianyuan Bo, Yuan Fang
In the rapidly evolving field of self-supervised learning on graphs, generative and contrastive methodologies have emerged as two dominant approaches. Our study focuses on masked f…
A Probabilistic Framework for Temporal Distribution Generalization in Industry-Scale Recommender Systems
Yuxuan Zhu, Cong Fu, Yabo Ni +2
Temporal distribution shift (TDS) erodes the long-term accuracy of recommender systems, yet industrial practice still relies on periodic incremental training, which struggles to ca…
A Contrastive Framework with User, Item and Review Alignment for Recommendation
Hoang V. Dong, Yuan Fang, Hady W. Lauw
Learning effective latent representations for users and items is the cornerstone of recommender systems. Traditional approaches rely on user-item interaction data to map users and…
Advancing Molecular Graph-Text Pre-training via Fine-grained Alignment
Yibo Li, Yuan Fang, Mengmei Zhang +1
Understanding molecular structure and related knowledge is crucial for scientific research. Recent studies integrate molecular graphs with their textual descriptions to enhance mol…
Contrastive General Graph Matching with Adaptive Augmentation Sampling
Jianyuan Bo, Yuan Fang
Graph matching has important applications in pattern recognition and beyond. Current approaches predominantly adopt supervised learning, demanding extensive labeled data which can…
Diffusion-based Negative Sampling on Graphs for Link Prediction
Trung-Kien Nguyen, Yuan Fang
Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems, etc. Modern graph link…
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…