  Graph-structured data consisting of objects (i.e., nodes) and relationships
among objects (i.e., edges) are ubiquitous. Graph-level learning is a matter of
studying a collection of graphs instead of a single graph. Traditional
graph-level learning methods used to be the mainstream. However, with the
increasing scale and complexity of graphs, Graph-level Neural Networks (GLNNs,
deep learning-based graph-level learning methods) have been attractive due to
their superiority in modeling high-dimensional data. Thus, a survey on GLNNs is
necessary. To frame this survey, we propose a systematic taxonomy covering
GLNNs upon deep neural networks, graph neural networks, and graph pooling. The
representative and state-of-the-art models in each category are focused on this
survey. We also investigate the reproducibility, benchmarks, and new graph
datasets of GLNNs. Finally, we conclude future directions to further push
forward GLNNs. The repository of this survey is available at
https://github.com/GeZhangMQ/Awesome-Graph-level-Neural-Networks.
