3 citations · 4 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
TIDFormer: Exploiting Temporal and Interactive Dynamics Makes A Great Dynamic Graph Transformer
Jie Peng, Zhewei Wei, Yuhang Ye
Due to the proficiency of self-attention mechanisms (SAMs) in capturing dependencies in sequence modeling, several existing dynamic graph neural networks (DGNNs) utilize Transforme…
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…
Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification: Technical Report
Haipeng Ding, Zhewei Wei, Yuhang Ye
Graph Neural Networks (GNNs) play a pivotal role in graph-based tasks for their proficiency in representation learning. Among the various GNN methods, spectral GNNs employing polyn…