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Organizational structure of corporations has potential to provideimplications for dynamics and performance of corporate operations. However,this subject has remained unexplored because of the lack of readily availableorganization network datasets. To overcome the this gap, we developed a newheuristic image-processing method to extract and reconstruct organizationnetwork data from published organization charts. Our method analyzes a PDF fileof a corporate organization chart and detects text labels, boxes, connectinglines, and other objects through multiple steps of heuristically implementedimage processing. The detected components are reorganized together into aPython's NetworkX Graph object for visualization, validation and furthernetwork analysis. We applied the developed method to the organization charts ofall the listed firms in Japan shown in the ``Organization Chart/System DiagramHandbook'' published by Diamond, Inc., from 2008 to 2011. Out of the 10,008organization chart PDF files, our method was able to reconstruct 4,606organization networks (data acquisition success rate: 46%). For eachreconstructed organization network, we measured several network diagnostics,which will be used for further statistical analysis to investigate theirpotential correlations with corporate behavior and performance.

Extracting Network Structures from Corporate Organization Charts Using Heuristic Image Processing