论文标题
水分配系统的空间图卷积神经网络
Spatial Graph Convolution Neural Networks for Water Distribution Systems
论文作者
论文摘要
我们研究了基于稀疏信号作为代表性的机器学习挑战的水分配系统（WDS）中缺少价值估计的任务。基础图具有相当低的节点程度和高直径，而图中的信息在全球范围内相关，因此图神经网络面临着长期依赖性的挑战。我们根据消息传递提出了一个特定的体系结构，该架构为WDS域中的许多基准任务显示出色的结果。此外，我们研究了多跳的变化，该变化需要少得多的资源，并为大WDS图提供了途径。
We investigate the task of missing value estimation in graphs as given by water distribution systems (WDS) based on sparse signals as a representative machine learning challenge in the domain of critical infrastructure. The underlying graphs have a comparably low node degree and high diameter, while information in the graph is globally relevant, hence graph neural networks face the challenge of long-term dependencies. We propose a specific architecture based on message passing which displays excellent results for a number of benchmark tasks in the WDS domain. Further, we investigate a multi-hop variation, which requires considerably less resources and opens an avenue towards big WDS graphs.
