The group identification literature mostly revolves around the problem of identifying individuals in the community who belong to ethnic or religious groups. Here we use the same model framework to identify individuals who play key role in some sense. In particular we will focus on expert selection in social networks. Ethnic groups and expert groups need completely different approaches and different type of selection rules are successful for one and for the other. We argue that stability is a key property in expert selection. The idea is that experts are more effective in identifying each other, thus the selected individuals should support each other’s membership. We propose a parametric algorithm based on the so called top candidate relation. The parameter expresses how permissive we want to be in expert selection. The two limit cases are the stable set and the top candidate core. The former contains virtually everybody that can be considered as an expert, while the latter consists of the elite. We establish an axiomatization to show that the algorithm is theoretically well-founded. Furthermore we present a case study using citation data to demonstrate its effectiveness. We compare its performance with classical centrality measures.
International Journal of Game Theory – Springer Journals
Published: Jun 21, 2017
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