Enhancing topology adaptation in information-sharing social networks

Abstract

The advent of Internet and World Wide Web has led to unprecedent growth of the information available. People usually face the information overload by following a limited number of sources which best fit their interests. It has thus become important to address issues like who gets followed and how to allow people to discover new and better information sources. In this paper we conduct an empirical analysis on different on-line social networking sites, and draw inspiration from its results to present different source selection strategies in an adaptive model for social recommendation. We show that local search rules which enhance the typical topological features of real social communities give rise to network configurations that are globally optimal. These rules create networks which are effective in information diffusion and resemble structures resulting from real social systems.

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• Clustering in Complex Directed Networks
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• Small world yields the most effective information spreading
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Cited by in corpus (6)

• The Internet of People: A human and data-centric paradigm for the Next Generation Internet
• The Structure of Online Social Networks Mirror Those in the Offline World
• Influence of Reciprocal links in Social Networks
• Identifying a set of influential spreaders in complex networks
• Adaptive social recommendation in a multiple category landscape
• The role of taste affinity in agent-based models for social recommendation