4 papers
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…
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…