利用启发式图像处理从企业组织结构图中提取网络结构
Extracting Network Structures from Corporate Organization Charts Using Heuristic Image Processing
AI总结：
针对企业组织网络数据集匮乏的问题，提出启发式图像处理方法从PDF格式的组织结构图中提取重建网络数据，应用于日本上市公司图谱取得46%的成功率，为企业行为与绩效关联研究提供数据支撑。
摘要：
企业组织结构有望为企业运营的动态变化与绩效表现提供参考启示。然而，由于缺乏现成的组织网络数据集，该研究领域一直未得到充分探索。为弥补这一空白，我们开发了一种新的启发式图像处理方法，用于从已发布的组织结构图中提取并重建组织网络数据。该方法会分析企业组织结构图的PDF文件，通过多步启发式实现的图像处理流程，检测文本标签、方框、连接线及其他对象。检测到的组件会被重新整合为Python的NetworkX Graph对象，用于可视化、验证及后续网络分析。我们将所开发的方法应用于Diamond, Inc.出版的《组织结构图/系统图手册》中2008年至2011年日本所有上市公司的组织结构图。在10008份组织结构图PDF文件中，我们的方法成功重建了4606个组织网络（数据获取成功率：46%）。针对每个重建的组织网络，我们测量了多项网络诊断指标，这些指标将用于后续的统计分析，以探究其与企业行为和绩效之间的潜在关联。
英文摘要：
Organizational structure of corporations has potential to provide implications for dynamics and performance of corporate operations. However, this subject has remained unexplored because of the lack of readily available organization network datasets. To overcome the this gap, we developed a new heuristic image-processing method to extract and reconstruct organization network data from published organization charts. Our method analyzes a PDF file of a corporate organization chart and detects text labels, boxes, connecting lines, and other objects through multiple steps of heuristically implemented image processing. The detected components are reorganized together into a Python's NetworkX Graph object for visualization, validation and further network analysis. We applied the developed method to the organization charts of all the listed firms in Japan shown in the ``Organization Chart/System Diagram Handbook'' published by Diamond, Inc., from 2008 to 2011. Out of the 10,008 organization chart PDF files, our method was able to reconstruct 4,606 organization networks (data acquisition success rate: 46%). For each reconstructed organization network, we measured several network diagnostics, which will be used for further statistical analysis to investigate their potential correlations with corporate behavior and performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
