Abstract
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.
Highlights
Social network analysis has become a joint focus of many branches of science [1,2]
In this work we focus on the so-called leadership networks which capture how people copy actions or receive information from others
They play a significant role in formation and propagation of social opinions, leadership networks have received considerably less attention than other social networks–possibly because of the lack of empirical data
Summary
Social network analysis has become a joint focus of many branches of science [1,2]. Various social networks have been systematically investigated, such as friendship, membership and co-authorship networks. The model proposed and investigated here mimics information spreading process in adaptive social networks. We evaluate its efficiency in filtering out the low-quality and irrelevant information and show that this distributed social recommender model can enhance the user experience.
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