2 citations · 5 across the 8 of their papers we have counts for
10 papers
Can Generative Recommendation Reach Cold Items? A Temporal Perspective on Semantic-ID Generation
Jie Peng, Yanping Zheng, Zhewei Zhe +3
Semantic-ID-based generative recommendation represents items as sequences of shared semantic tokens, enabling token recombination beyond isolated item IDs. However, closed-world re…
Lighter-X: An Efficient and Plug-and-play Strategy for Graph-based Recommendation through Decoupled Propagation
Yanping Zheng, Zhewei Wei, Frank de Hoog +4
Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness in recommendation systems. However, conventional graph-based recommenders, such as LightGCN, require maintai…
Future Link Prediction Without Memory or Aggregation
Lu Yi, Runlin Lei, Fengran Mo +3
Future link prediction on temporal graphs is a fundamental task with wide applicability in real-world dynamic systems. These scenarios often involve both recurring (seen) and novel…
TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics
Lu Yi, Jie Peng, Yanping Zheng +5
Future link prediction is a fundamental challenge in various real-world dynamic systems. To address this, numerous temporal graph neural networks (temporal GNNs) and benchmark data…
Rethinking Link Prediction for Directed Graphs
Mingguo He, Yuhe Guo, Yanping Zheng +3
Link prediction for directed graphs is a crucial task with diverse real-world applications. Recent advances in embedding methods and Graph Neural Networks (GNNs) have shown promisi…
QAGCF: Graph Collaborative Filtering for Q&A Recommendation
Changshuo Zhang, Teng Shi, Xiao Zhang +5
Question and answer (Q&A) platforms usually recommend question-answer pairs to meet users' knowledge acquisition needs, unlike traditional recommendations that recommend only one i…
A survey of dynamic graph neural networks
Yanping Zheng, Lu Yi, Zhewei Wei
Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and learning from graph-structured data, with applications spanning numerous domains. However, m…
GTP-ViT: Efficient Vision Transformers via Graph-based Token Propagation
Xuwei Xu, Sen Wang, Yudong Chen +3
Vision Transformers (ViTs) have revolutionized the field of computer vision, yet their deployments on resource-constrained devices remain challenging due to high computational dema…
Decoupled Graph Neural Networks for Large Dynamic Graphs
Yanping Zheng, Zhewei Wei, Jiajun Liu
Real-world graphs, such as social networks, financial transactions, and recommendation systems, often demonstrate dynamic behavior. This phenomenon, known as graph stream, involves…
Instant Graph Neural Networks for Dynamic Graphs
Yanping Zheng, Hanzhi Wang, Zhewei Wei +2
Graph Neural Networks (GNNs) have been widely used for modeling graph-structured data. With the development of numerous GNN variants, recent years have witnessed groundbreaking res…