Emergence of scale-free leadership structure in social recommender systems

Abstract

The study of the organization of social networks is important for 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.

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Cited by in corpus (20)

• Recommender Systems
• Diversity of individual mobility patterns and emergence of aggregated scaling laws
• Potential Theory for Directed Networks
• True scale-free networks hidden by finite size effects
• Prediction in complex systems: the case of the international trade network
• Influence of Reciprocal links in Social Networks
• Dense Power-law Networks and Simplicial Complexes
• Enhancing topology adaptation in information-sharing social networks
• Empirical studies on the network of social groups: the case of Tencent QQ
• The essential role of time in network-based recommendation
• Information filtering in sparse online systems: recommendation via semi-local diffusion
• Emergence of scale-free close-knit friendship structure in online social networks
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• Binary decision making with very heterogeneous influence
• A mechanism for evolution of the physical concepts network
• The role of taste affinity in agent-based models for social recommendation