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Minji Yoon

2 accepted papers

2023

Graph Generative Model for Benchmarking Graph Neural Networks

ICML 2023poster

As the field of Graph Neural Networks (GNN) continues to grow, it experiences a corresponding increase in the need for large, real-world datasets to train and test new GNN models on challenging, realistic problems. Unfortunately, such graph datasets are often generated from online, highly privacy-re…

2022

Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks

NeurIPS 2022accept

Data continuously emitted from industrial ecosystems such as social or e-commerce platforms are commonly represented as heterogeneous graphs (HG) composed of multiple node/edge types. State-of-the-art graph learning methods for HGs known as heterogeneous graph neural networks (HGNNs) are applied to…