Emergence of scale-free leadership structure in social recommender systems
Zhou, TaoWeb Sciences Center, University of Electronic Science and Technology of China, Chengdu, China - Department of Physics, University of Fribourg, Switzerland - Department of Modern Physics, University of Science and Technology of China, Hefei, China
Medo, MatúšDepartment of Physics, University of Fribourg, Switzerland
Cimini, GiulioDepartment of Physics, University of Fribourg, Switzerland
Zhang, Zi-KeWeb Sciences Center, University of Electronic Science and Technology of China, Chengdu, China - Department of Physics, University of Fribourg, Switzerland
Zhang, Yi-ChengWeb Sciences Center, University of Electronic Science and Technology of China, Chengdu, China - Department of Physics, University of Fribourg, Switzerland
27.07.2011
Published in:
PLoS ONE. - 2011, vol. 6, no. 7, p. e20648
English
The study of the organization of social networks is important for the understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this feature, we propose an adaptive network model driven by social recommending. Artificial agent-based simulations of this model highlight a “good get richer” mechanism where users with broad interests and good judgments are likely to become popular leaders for the others. Simulations also indicate that the studied social recommendation mechanism can gradually improve the user experience by adapting to tastes of its users. Finally we outline implications for real online resource-sharing systems.