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Analysis of Control Flow Graphs Using Graph Convolutional Neural Networks

  • With the digital transformation of companies, ever larger amounts of data are generated and available for analysis. Process mining techniques can be used to extract and analyze process models from these data. Related techniques have quickly developed into an important field with constantly increasing investments in recent years. Thus, the automated analysis of processes has gained an important role in many companies. In this context, graphs have been shown to be an intuitive representation of how the gathered processes are carried out using the aforementioned techniques. For the analysis of these so-called control flow graphs, we investigate the use of convolution neural networks, which are specially designed for graphs: graph convolution networks (GCNs). In our contribution, GCNs are used to perform a regression task based on individual control flows of a process in which farmers apply for specific governmental payments. The approach achieved promising results on this publicly available data set.

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Metadaten
Author:Patrick Philipp, Rafael X. Morales Georgi, Jürgen Beyerer, Sebastian Robert, Jürgen Beyerer
URL:https://doi.org/10.1109/ISCMI47871.2019.9004296
Parent Title (English):2019 6th International Conference on Soft Computing & Machine Intelligence (ISCMI)
Document Type:Conference Proceeding
Language:English
Publication Year:2019
Tag:control flow; convolutional neural networks; digital transformation; process analysis; process mining; workflow
First Page:73
Last Page:77