  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.
