BriefGPT.xyz
Jul, 2019
了解图神经网络在学习图拓扑方面的表征能力
Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
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Nima Dehmamy, Albert-László Barabási, Rose Yu
TL;DR
通过研究图卷积网络的表示能力，我们发现GCNs在学习图瞬间时有一定限制，而使用不同传播规则的模块化GCN设计可以显著提高其性能和表示能力。
Abstract
To deepen our understanding of graph neural networks, we investigate the representation power of
graph convolutional networks
(GCN) through the looking glass of
graph moments
, a key property of graph topology enc
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