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

TL;DR

This paper introduces a novel excitable sensor network approach inspired by human sensations to detect and rank spreading influence in social networks, outperforming traditional methods in simulations and real-world data.

Contribution

The paper proposes a new excitable sensor network method for spreading detection, demonstrating its effectiveness over existing strategies in diverse social network scenarios.

Findings

Better detection of small-scale spreading processes.

Effective distinction of large-scale diffusion due to self-inhibition.

Outperforms traditional sensor placement methods in simulations and real data.

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.