A fast approach for multi-objective team formation in social networks

In recent years, the growth and popularity of social networks have created a new world of collaboration and communication. Team formation is a new research topic in the area of social network analysis. Consider there is a social network of experts and the goal is to form the best possible team out of them for a given project. The best solution is a team with the minimized communication cost within team members. A social network is modeled as a graph, in which nodes represent experts and an edge between two nodes shows a prior collaboration of the two experts. The contribution of this paper is to select team members based on both closeness centrality and eigenvector centrality. Experimental results on the DBLP dataset show that our approach completes in lower time compared to the previous methods.