Bio: Dr. Rami Puzis - Lecturer, BSc Software Engineering, MSc Information Systems Engineering and PhD topic on Deployment of Intrusion Detection Systems. He has worked as a research associate in the Laboratory of Computational Cultural Dynamics, University of Maryland. His primary specialization is in the area of complex networks with applications to cybersecurity, social and communication network analysis. He has been the principal investigator of a series of research projects funded by Deutsche Telekom AG, Israeli Ministry of Defense, Israeli Ministry of Economy, and several leading cybersecurity industries.
Organization Mining Using Online Social Networks
Abstract: Complementing the formal organizational structure of a business are the informal connections among employees. These relationships help identify knowledge hubs, working groups, and shortcuts through the organizational structure. They carry valuable information on how a company functions de-facto. In the past, eliciting the informal social networks within an organization was challenging; today they are reflected by friendship relationships in online social networks. In this paper we analyze several commercial organizations by mining data which their employees have exposed on Facebook, LinkedIn, and other publicly available sources. Using a web crawler designed for this purpose, we extract a network of informal social relationships among employees of targeted organizations. Our results show that it is possible to identify leadership roles within the organization solely by using centrality analysis and machine learning techniques applied to the informal relationship network structure. Valuable non-trivial insights can also be gained by clustering an organization’s social network and gathering publicly available information on the employees within each cluster. Knowledge of the network of informal relationships may be a major asset or might be a significant threat to the underlying organization.
