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Dustin Zelle

2 accepted papers

2023

Learning Large Graph Property Prediction via Graph Segment Training

NeurIPS 2023poster

Learning to predict properties of large graphs is challenging because each prediction requires the knowledge of an entire graph, while the amount of memory available during training is bounded. Here we propose Graph Segment Training (GST), a general framework that utilizes a divide-and-conquer appro…

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…