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.
Highlights
A significant challenge in any organization is identifying the underlying organizational structure that might be different from the official version
Social network analysis (SNA) examining structural features, Diesner and Carley [6], has been applied to extract properties of the Enron network and attempts to detect the key players around the time of Enron’s crisis: Diesner et al [7] studied the patterns of communication of Enron employees differentiated by their hierarchical level; Cotterill [8] investigated a set of stylistic language features to predict organizational hierarchy relationships; Danescu-NiculescuMizil [9] presented an analysis framework on linguistic coordination to analyze power relationships in static and situational forms; Chundi et al [1] conducted a time series
A particular case is the legal team because it does not include any senior managers; it has about the same average CorpRank score as the executive VPs and managing directors. is can be explained considering the central role that this professional group played while Enron hid its losses using new financial vehicles and filed for bankruptcy in 2001
Summary
A significant challenge in any organization is identifying the underlying organizational structure that might be different from the official version. Others have studied the application of social networks to practical corporate improvement methods as follows: Hovelynck and Chidlovskii [35] adopted commonly used features of nodes to represent key properties of actors in response to this work, assigning a social score for each node to improve classification performance; Li and Somayaji [36] applied SNA to organizational access control; Michalski et al [37] matched social network hierarchies in organizations with a stable corporate structure to improve company management; Rivera-Pelayo [38] considered the application of data mining and SNA for its program, ExpertSN, allowing for effective people search in a given work context; Ganjaliyev [39] proposed a new method to identify network communities to enhance social network analysis; and Wang et al [40] used HumanRank, a method of ranking individuals based on importance using personal electronic interactions. E rest of the paper is organized as follows: Section 2 describes the dataset; Section 3 presents the CorpRank algorithm; Section 4 describes the research design; Section 5 presents the results; Section 6 discusses the results, and Section 7 concludes the paper
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