7 citations · 13 across the 5 of their papers we have counts for
6 papers
What Makes LLMs Effective Sequential Recommenders? A Study on Preference Intensity and Temporal Context
Zhongyu Ouyang, Qianlong Wen, Chunhui Zhang +2
What enables large language models (LLMs) to effectively model user preferences in sequential recommendation? Our investigation reveals that existing preference-alignment approache…
Self-Supervised Graph Structure Refinement for Graph Neural Networks
Jianan Zhao, Qianlong Wen, Mingxuan Ju +2
Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural networks (GNNs), has shown great potential in boosting the performance of GNNs. Most exist…
Graph Contrastive Learning with Cross-view Reconstruction
Qianlong Wen, Zhongyu Ouyang, Chunhui Zhang +3
Among different existing graph self-supervised learning strategies, graph contrastive learning (GCL) has been one of the most prevalent approaches to this problem. Despite the rema…
Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization
Mingxuan Ju, Tong Zhao, Qianlong Wen +4
Self-supervised learning (SSL) for graph neural networks (GNNs) has attracted increasing attention from the graph machine learning community in recent years, owing to its capabilit…
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning
Chunhui Zhang, Chao Huang, Yijun Tian +5
Even pruned by the state-of-the-art network compression methods, Graph Neural Networks (GNNs) training upon non-Euclidean graph data often encounters relatively higher time costs,…
Clinical connectivity map for drug repurposing: using laboratory tests to bridge drugs and diseases
Qianlong Wen, Ruoqi Liu, Ping Zhang
Drug repurposing has attracted increasing attention from both the pharmaceutical industry and the research community. Many existing computational drug repurposing methods rely on p…