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.

这篇研究论文通过开发一种新的启发式图像处理方法来提取和重构从已发布的组织结构图中获取的组织网络数据，然后应用于日本上市公司的组织结构图，成功重构了 4606 个组织网络，并测量了多个网络诊断指标，以进一步研究其与企业行为和绩效的相关性。

利用启发式图像处理从企业组织图中提取网络结构

Extracting Network Structures from Corporate Organization Charts Using  Heuristic Image Processing

We are concerned with the discovery of hierarchical relationships from
large-scale unstructured similarity scores. For this purpose, we study
different models of hyperbolic space and find that learning embeddings in the
Lorentz model is substantially more efficient than in the Poincar\'e-ball
model. We show that the proposed approach allows us to learn high-quality
embeddings of large taxonomies which yield improvements over Poincar\'e
embeddings, especially in low dimensions. Lastly, we apply our model to
discover hierarchies in two real-world datasets: we show that an embedding in
hyperbolic space can reveal important aspects of a company's organizational
structure as well as reveal historical relationships between language families.

研究了使用超几何空间模型学习大型分类体系中的嵌入向量，其中 Lorentz 模型的效率高于 Poincaré-ball 模型，并发现该方法可以应用于发现公司组织结构和语言家族之间的历史关系。