Future Automation Engineering using Structural Graph Convolutional Neural Networks
This work addresses the need for automated analysis of interconnected engineering data in automation engineering, though it appears incremental as it builds on existing graph learning methods.
The paper tackles the problem of classifying and clustering engineering data artifacts represented as graphs by introducing a Structural Graph Convolutional Neural Network (SGCNN) with a novel convolution kernel and pooling algorithm, achieving approximately 91% classification accuracy on a realistic dataset.
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.