A Hierarchy of Graph Neural Networks Based on Learnable Local Features

Li Michael Lingzhi
Li Michael Lingzhi
Dong Meng
Dong Meng
Zhou Jiawei
Zhou Jiawei
Cited by: 0|Bibtex|Views68
Other Links: arxiv.org

Abstract:

Graph neural networks (GNNs) are a powerful tool to learn representations on graphs by iteratively aggregating features from node neighbourhoods. Many variant models have been proposed, but there is limited understanding on both how to compare different architectures and how to construct GNNs systematically. Here, we propose a hierarchy...More

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