Authors: Ibrahim Kamel; Zaher Al Aghbari; Kareem Kamel
Addresses: Department of Computer and Electrical Engineering, College of Engineering, University of Sharjah, Sharjah, UAE ' Department of Computer Science, College of Sciences, University of Sharjah, Sharjah, UAE ' Department of Computer Science and Engineering, University of California, San Diego, USA
Abstract: This paper presents a realistic team formation algorithm that navigates through a social network graph to select a team of experts to work in a target project. The project is represented with a set of skills that are required for the project implementation. Each node in the graph represents an individual who has one or more skills. Individuals (nodes) connect with friends who might share some common skills. Unlike most of the prior works in this area, the proposed algorithm assumes a local view of the network resulting in an absence of pre-computed network statistics. The proposed algorithm uses homophily in navigation to reach to relevant nodes. We use a distance function to quantify the similarity between two skills guided by WordNet ontology. The experiments show that the proposed algorithm reaches to the required team in up to 20% less hubs than the breadth first search.
Keywords: social network analysis; SNA; team formation; graph navigation; graph processing; skill similarity; project skills; homophily; ontology; project teams.
International Journal of Big Data Intelligence, 2016 Vol.3 No.4, pp.228 - 238
Received: 24 Sep 2015
Accepted: 06 Feb 2016
Published online: 21 Oct 2016 *