Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks

Abstract

Detecting spreading outbreaks in social networks with sensors is of great significance in applications. Inspired by the formation mechanism of human's physical sensations to external stimuli, we propose a new method to detect the influence of spreading by constructing excitable sensor networks. Exploiting the amplifying effect of excitable sensor networks, our method can better detect small-scale spreading processes. At the same time, it can also distinguish large-scale diffusion instances due to the self-inhibition effect of excitable elements. Through simulations of diverse spreading dynamics on typical real-world social networks (facebook, coauthor and email social networks), we find that the excitable senor networks are capable of detecting and ranking spreading processes in a much wider range of influence than other commonly used sensor placement methods, such as random, targeted, acquaintance and distance strategies. In addition, we validate the efficacy of our method with diffusion data from a real-world online social system, Twitter. We find that our method can detect more spreading topics in practice. Our approach provides a new direction in spreading detection and should be useful for designing effective detection methods.

References in corpus (5)

• Leaders in Social Networks, the Delicious Case
• Social Network Sensors for Early Detection of Contagious Outbreaks
• Searching for superspreaders of information in real-world social media
• Epidemic spreading on heterogeneous networks with identical infectivity
• Dynamical Immunization Strategy for Seasonal Epidemics

Cited by in corpus (4)

• Exploring the complex pattern of information spreading in online blog communities
• Dynamic range maximization in excitable networks
• Dynamic regulation of resource transport induces criticality in interdependent networks of excitable units
• Backtracking activation impacts the criticality of excitable networks