515 citations · 580 across the 6 of their papers we have counts for
4 papers · 1 filter
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction
Seongjun Yun, Seoyoon Kim, Junhyun Lee +2
Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graph-structured data. They have shown significant improvements over traditional heuristic…
Deformable Graph Transformer
Jinyoung Park, Seongjun Yun, Hyeonjin Park +5
Transformer-based models have recently shown success in representation learning on graph-structured data beyond natural language processing and computer vision. However, the succes…
Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs
Seongjun Yun, Minbyul Jeong, Sungdong Yoo +5
Graph Neural Networks (GNNs) have been widely applied to various fields due to their powerful representations of graph-structured data. Despite the success of GNNs, most existing G…
Graph Transformer Networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim +2
Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved state-of-the-art performance in tasks such as node classification and link pred…