  Graph neural networks (GNNs) are effective machine learning models for many
graph-related applications. Despite their empirical success, many research
efforts focus on the theoretical limitations of GNNs, i.e., the GNNs expressive
power. Early works in this domain mainly focus on studying the graph
isomorphism recognition ability of GNNs, and recent works try to leverage the
properties such as subgraph counting and connectivity learning to characterize
the expressive power of GNNs, which are more practical and closer to
real-world. However, no survey papers and open-source repositories
comprehensively summarize and discuss models in this important direction. To
fill the gap, we conduct a first survey for models for enhancing expressive
power under different forms of definition. Concretely, the models are reviewed
based on three categories, i.e., Graph feature enhancement, Graph topology
enhancement, and GNNs architecture enhancement.
