论文标题
图形神经网络：当前的进度和未来方向
Graph-level Neural Networks: Current Progress and Future Directions
论文作者
论文摘要
图形结构的数据由对象（即节点）和对象之间的关系（即边缘）之间的关系无处不在。图级学习是研究图的集合而不是单个图形的问题。传统的图形学习方法曾经是主流。但是，随着图表的规模和复杂性的提高，图形级神经网络（GLNN，基于深度学习的图形学习方法）由于它们在建模高维数据方面的优势而具有吸引力。因此，必须对GLNNS进行调查。为了构建这项调查，我们提出了一个系统的分类法，涵盖了深度神经网络，图形神经网络和图形池的GLNN。每个类别中的代表和最先进的模型都集中在此调查上。我们还研究了GLNNS的可重复性，基准和新图形数据集。最后，我们总结了未来的方向，以进一步推动GLNNS。该调查的存储库可从https://github.com/gezhangmq/awesome-graph-level-neural-networks获得。
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.
