116 citations · 146 across the 10 of their papers we have counts for
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cs.LG2022
Models and Benchmarks for Representation Learning of Partially Observed Subgraphs
Dongkwan Kim, Jiho Jin, Jaimeen Ahn +1
Subgraphs are rich substructures in graphs, and their nodes and edges can be partially observed in real-world tasks. Under partial observation, existing node- or subgraph-level mes…
cs.LG2022★ 116 cited
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision
Dongkwan Kim, Alice Oh
Attention mechanism in graph neural networks is designed to assign larger weights to important neighbor nodes for better representation. However, what graph attention learns is not…
cs.LG2022★ 3 cited
Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning
Dongkwan Kim, Alice Oh
Subgraph representation learning has emerged as an important problem, but it is by default approached with specialized graph neural networks on a large global graph. These models d…