Abstract. We tackle the problem of finding a team of experts from a social network to complete a project that requires a set of skills. The social network is modeled as a graph. A node in the graph represents an expert and has a weight representing the monetary cost for using the expert service. Two nodes in the graph can be connected and the weight on the edge represents the communication cost between the two corresponding experts. Given a project, our objective is to find a team of experts that covers all the required skills and also minimizes the communication cost as well as the personnel cost of the project. To minimize both of the objectives, we define a new combined cost function which is based on the linear combination of the objectives (i.e. communication and personnel costs). We show that the problem of minimizing the combined cost function is an NP-hard problem. Thus, one approximation algorithm is proposed to solve the problem. The proposed approximation algorithm is bounded and the approximation ratio of the algorithm is proved in the paper. Three heuristic algorithms based on different intuitions are also proposed for solving the problem. Extensive experiments on real datasets demonstrate the effectiveness and scalability of the proposed algorithms.
In REF , the authors propose to find a team of experts while minimizing both communication and personnel cost.
Efficient bi-objective team formation in social networks
ECML/PKDD
Computer Science