8 citations · 43 across the 12 of their papers we have counts for
5 papers · 1 filter
Meta-node: A Concise Approach to Effectively Learn Complex Relationships in Heterogeneous Graphs
Jiwoong Park, Jisu Jeong, Kyungmin Kim +1
Existing message passing neural networks for heterogeneous graphs rely on the concepts of meta-paths or meta-graphs due to the intrinsic nature of heterogeneous graphs. However, th…
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…
Metropolis-Hastings Data Augmentation for Graph Neural Networks
Hyeonjin Park, Seunghun Lee, Sihyeon Kim +5
Graph Neural Networks (GNNs) often suffer from weak-generalization due to sparsely labeled data despite their promising results on various graph-based tasks. Data augmentation is a…
Global-Local Item Embedding for Temporal Set Prediction
Seungjae Jung, Young-Jin Park, Jisu Jeong +4
Temporal set prediction is becoming increasingly important as many companies employ recommender systems in their online businesses, e.g., personalized purchase prediction of shoppi…
div2vec: Diversity-Emphasized Node Embedding
Jisu Jeong, Jeong-Min Yun, Hongi Keam +3
Recently, the interest of graph representation learning has been rapidly increasing in recommender systems. However, most existing studies have focused on improving accuracy, but i…