Enhancing topology adaptation in information-sharing social networks

TL;DR

This paper investigates how local search rules can improve the structure of social networks for better information diffusion, leading to more effective social recommendation systems inspired by real-world social community features.

Contribution

It introduces adaptive source selection strategies based on local search rules that enhance network topology for improved information sharing.

Findings

Local search rules produce network structures similar to real social communities.

Enhanced topologies facilitate more effective information diffusion.

Networks with these rules outperform traditional models in social recommendation tasks.

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.