Ryoma Sato
Published 2020-03-09Version 1
Graph neural networks (GNNs) are effective machine learning models for various graph learning problems. Despite their empirical successes, the theoretical limitations of GNNs have been revealed recently. Consequently, many GNN models have been proposed to overcome these limitations. In this survey, we provide a comprehensive overview of the expressive power of GNNs and provably powerful variants of GNNs.
The expressive power of pooling in Graph Neural Networks
On the Expressive Power of Graph Neural Networks
Uplifting the Expressive Power of Graph Neural Networks through Graph Partitioning
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