On finding the strongly connected components in a directed graph
Information Processing Letters
An Empirical Study of Collusion Behavior in the Maze P2P File-Sharing System
ICDCS '07 Proceedings of the 27th International Conference on Distributed Computing Systems
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
IBM Journal of Research and Development - Business optimization
Predicting tie strength with social media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Modeling relationship strength in online social networks
Proceedings of the 19th international conference on World wide web
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Suggesting friends using the implicit social graph
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Collusion Detection and Prevention with FIRE+ Trust and Reputation Model
CIT '10 Proceedings of the 2010 10th IEEE International Conference on Computer and Information Technology
Constructing expert profiles over time for skills management and expert finding
i-KNOW '11 Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies
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Organizations need to accurately understand the skills and competencies of their human resources in order to effectively respond to internal and external demands for expertise and make informed hiring decisions. In recent years, however, human resources have become highly mobile, making it more difficult for organizations to accurately learn their competencies. In such environment, organizations need to rely significantly on third parties to provide them with useful information about individuals. These sources and the information they provide, however, vary in degrees of trust and validity. In a previous paper, we developed an ontology for skills and competencies and modeled and analyzed the various sources of information used to derive the belief in an individual's level of competency. In this paper, we present an approach based on social network analysis for identifying unreliable sources of competency information. We explore the conditions under which evaluations given by an individual or a group about another can be trusted. We evaluate this approach using recommendation data gathered by crawling user profiles in LinkedIn.