3 papers
cs.LG2025
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…
cs.LG2024
How Hard Is It for Message-Passing GNNs to Simulate One Weisfeiler-Lehman Color-Refinement Step?
Guanyu Cui, Yuhe Guo, Zhewei Wei +1
Message-passing graph neural networks (MPGNNs) are commonly compared with the Weisfeiler-Lehman (WL) color-refinement procedure, but this comparison does not quantify the resource…
cs.LG2022
Clenshaw Graph Neural Networks
Yuhe Guo, Zhewei Wei
Graph Convolutional Networks (GCNs), which use a message-passing paradigm with stacked convolution layers, are foundational methods for learning graph representations. Recent GCN m…