This paper provides a novel algorithm for automatically extracting social hierarchy data from electronic communication behavior. The algorithm is based on data mining user behaviors to automatically analyze and catalog patterns of communications between entities in a email collection to extract social standing. The advantage to such automatic methods is that they extract relevancy between hierarchy levels and are dynamic over time.We illustrate the algorithms over real world data using the Enron corporation's email archive. The results show great promise when compared to the corporations work chart and judicial proceeding analyzing the major players.
We present our work on automatically extracting social hierarchies from electronic communication data. Data mining based on user behavior can be leveraged to analyze and catalog patterns of communications between entities to rank relationships. The advantage is that the analysis can be done in an automatic fashion and can adopt itself to organizational changes over time. We illustrate the algorithms over real world data using the Enron corporation's email archive. The results show great promise when compared to the corporations work chart and judicial proceeding analyzing the major players.
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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