A team discovery model for crowdsourcing tasks to social networks

Abstract

Social network has emerged as an important paradigm in modern business operation. Outsourcing tasks to social network helps organisations to mitigate the shortage of skill or expertise in some domain. Expert team discovery is an important problem in complex collaborative networks. Existing expert team discovery models need to traverse every candidate in expert network until the optimal team solution is found, which would lead to high computational cost. In this paper, a team formation model is proposed to outsource tasks to social networks. In order to contract search space of team formation for seeded candidates, the proposed model selects centrality expert list as seed to reduce the communication cost. Moreover, based on the notion of Skyline, the proposed model can effectively and efficiently identify experts by reducing the number of expert candidates. Theoretical analysis and extensive experiments on real and synthetically generated dataset demonstrate the effectiveness and scalability of the proposed method.