Community discovery for knowledge collaborations in collective intelligence systems

Abstract

Knowledge collaborative communities play an important role in collective intelligence systems. To discover a knowledge collaborative community, we need to consider not only the structure of a network but also the performance of knowledge collaboration among members within the community. Traditional community discovery approaches are not suitable to discover knowledge collaborative communities since most of them focus too much on the network topologies, and ignore some other important factors. In this paper, we propose two community discovery approaches, which can be used in different sizes of networks, and take more knowledge collaboration factors into account. Compared with some other existing approaches, the proposed approach can perform better in forming knowledge collaborative communities for multi-domain problem solving. © 2014 Information Processing Society of Japan.