Two-Phase Pareto Set Discovery for Team Formation in Social Networks

In this paper, we study the problem of finding teams of experts from an expert network while optimizing three objectives. Given a project, the objective is to find teams of experts that cover all the required skills and also optimize the communication cost as well as the personnel cost and the expertise level of the team members. The expert network is modeled as a graph, where nodes represent experts and edges between nodes specify the communication costs between the experts. In this paper, we are interested in finding a Pareto front of teams that not only cover the required skills but are also not dominated by other feasible teams with respect to the three criteria. Since the problem is NP-hard, we propose algorithms to use with a two-phase method to find an approximation of the Pareto front for the three criteria team formation problem. In the first phase, an initial population which is composed of an approximation of the supported efficient teams is generated. Then, a Pareto local search method is applied to each solution of the initial population to find other members of the Pareto front. The proposed method is evaluated on the DBLP data set. The results indicate its superior performance comparing with other methods in terms of running time and the quality of answers.