Discovering Organizational Hierarchy through a Corporate Ranking Algorithm: The Enron Case

Germán G. Creamer; Salvatore J. Stolfo; Mateo Creamer; Shlomo Hershkop; Ryan Rowe

doi:10.1155/2022/8154476

Discovering Organizational Hierarchy through a Corporate Ranking Algorithm: The Enron Case

Germán G. Creamer, Salvatore J. Stolfo, Mateo Creamer, Shlomo Hershkop, Ryan Rowe

Stevens Institute of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

This paper proposes the CorpRank algorithm to extract social hierarchies from electronic communication data. The algorithm computes a ranking score for each user as a weighted combination of the number of emails, the number of responses, average response time, clique scores, and several degree and centrality measures. The algorithm uses principal component analysis to calculate the weights of the features. This score ranks users according to their importance, and its output is used to reconstruct an organization chart. We illustrate the algorithm over real-world data using the Enron corporation's e-mail archive. Compared to the actual corporate work chart, compensation lists, judicial proceedings, and analyzing the major players involved, the results show promise.

Original language	English
Article number	8154476
Journal	Complexity
Volume	2022
DOIs	https://doi.org/10.1155/2022/8154476
State	Published - 2022

All Science Journal Classification (ASJC) codes

General Computer Science
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title = "Discovering Organizational Hierarchy through a Corporate Ranking Algorithm: The Enron Case",

abstract = "This paper proposes the CorpRank algorithm to extract social hierarchies from electronic communication data. The algorithm computes a ranking score for each user as a weighted combination of the number of emails, the number of responses, average response time, clique scores, and several degree and centrality measures. The algorithm uses principal component analysis to calculate the weights of the features. This score ranks users according to their importance, and its output is used to reconstruct an organization chart. We illustrate the algorithm over real-world data using the Enron corporation's e-mail archive. Compared to the actual corporate work chart, compensation lists, judicial proceedings, and analyzing the major players involved, the results show promise.",

author = "Creamer, {Germ{\'a}n G.} and Stolfo, {Salvatore J.} and Mateo Creamer and Shlomo Hershkop and Ryan Rowe",

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year = "2022",

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language = "English",

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AU - Hershkop, Shlomo

AU - Rowe, Ryan

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AB - This paper proposes the CorpRank algorithm to extract social hierarchies from electronic communication data. The algorithm computes a ranking score for each user as a weighted combination of the number of emails, the number of responses, average response time, clique scores, and several degree and centrality measures. The algorithm uses principal component analysis to calculate the weights of the features. This score ranks users according to their importance, and its output is used to reconstruct an organization chart. We illustrate the algorithm over real-world data using the Enron corporation's e-mail archive. Compared to the actual corporate work chart, compensation lists, judicial proceedings, and analyzing the major players involved, the results show promise.

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Discovering Organizational Hierarchy through a Corporate Ranking Algorithm: The Enron Case

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