2015 OrganizationalChartInference

• (Zhang et al., 2015) ⇒ Jiawei Zhang, Philip S. Yu, and Yuanhua Lv. (2015). “Organizational Chart Inference.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783266

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Cited By

• http://scholar.google.com/scholar?q=%222015%22+Organizational+Chart+Inference
• http://dl.acm.org/citation.cfm?id=2783258.2783266&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; data mining; enterprise social network; organization chart inference

Abstract

Nowadays, to facilitate the communication and cooperation among employees, a new family of online social networks has been adopted in many companies, which are called the “enterprise social networks " (ESNs). ESNs can provide employees with various professional services to help them deal with daily work issues. Meanwhile, employees in companies are usually organized into different hierarchies according to the relative ranks of their positions. The company internal management structure can be outlined with the organizational chart visually, which is normally confidential to the public out of the privacy and security concerns. In this paper, we want to study the IOC(Inference of Organizational Chart) problem to identify company internal organizational chart based on the heterogeneous online ESN launched in it. IOC is very challenging to address as, to guarantee smooth operations, the internal organizational charts of companies need to meet certain structural requirements (about its depth and width). To solve the IOC problem, a novel unsupervised method Create (ChArT REcovEr) is proposed in this paper, which consists of 3 steps: (1) social stratification of ESN users into different social classes, (2) supervision link inference from managers to subordinates, and (3) consecutive social classes matching to prune the redundant supervision links. Extensive experiments conducted on real-world online ESN dataset demonstrate that Create can perform very well in addressing the IOC problem.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 OrganizationalChartInference	Philip S. Yu Jiawei Zhang Yuanhua Lv			Organizational Chart Inference				10.1145/2783258.2783266		2015