Learning adaptive neighborhoods for graph neural networks

Learning adaptive neighborhoods for graph neural networks

Graph convolutional networks (GCNs) enable end-to-end learning on graph structured data. However, many works assume a given graph structure. When the input graph is noisy or unavailable, one approach is to construct or learn a latent graph structure. These methods typically fix the choice of node de...

Полное описание

Библиографические подробности
Главные авторы:	Saha, A, Mendez, O, Russell, C, Bowden, R
Формат:	Conference item
Язык:	English
Опубликовано:	IEEE 2024

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