22 citations · 37 across the 13 of their papers we have counts for
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cs.LG2023★ 9 cited
Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classification
Yoonhyuk Choi, Jiho Choi, Taewook Ko +1
Graph Neural Networks (GNNs) have proven to be powerful in many graph-based applications. However, they fail to generalize well under heterophilic setups, where neighbor nodes have…
cs.LG2023★ 1 cited
Signed Directed Graph Contrastive Learning with Laplacian Augmentation
Taewook Ko, Yoonhyuk Choi, Chong-Kwon Kim
Graph contrastive learning has become a powerful technique for several graph mining tasks. It learns discriminative representation from different perspectives of augmented graphs.…
cs.LG2023
Improving Signed Propagation for Graph Neural Networks in Multi-Class Environments
Yoonhyuk Choi, Jiho Choi, Taewook Ko +1
Message-passing Graph Neural Networks (GNNs), which collect information from adjacent nodes achieve dismal performance on heterophilic graphs. Various schemes have been proposed to…