Future Automation Engineering using Structural Graph Convolutional
  Neural Networks
  The digitalization of automation engineering generates large quantities of
engineering data that is interlinked in knowledge graphs. Classifying and
clustering subgraphs according to their functionality is useful to discover
functionally equivalent engineering artifacts that exhibit different graph
structures. This paper presents a new graph learning algorithm designed to
classify engineering data artifacts -- represented in the form of graphs --
according to their structure and neighborhood features. Our Structural Graph
Convolutional Neural Network (SGCNN) is capable of learning graphs and
subgraphs with a novel graph invariant convolution kernel and
downsampling/pooling algorithm. On a realistic engineering-related dataset, we
show that SGCNN is capable of achieving ~91% classification accuracy.
