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Detecting spreading outbreaks in social networks with sensors is of greatsignificance in applications. Inspired by the formation mechanism of human'sphysical sensations to external stimuli, we propose a new method to detect theinfluence of spreading by constructing excitable sensor networks. Exploitingthe amplifying effect of excitable sensor networks, our method can betterdetect small-scale spreading processes. At the same time, it can alsodistinguish large-scale diffusion instances due to the self-inhibition effectof excitable elements. Through simulations of diverse spreading dynamics ontypical real-world social networks (facebook, coauthor and email socialnetworks), we find that the excitable senor networks are capable of detectingand ranking spreading processes in a much wider range of influence than othercommonly used sensor placement methods, such as random, targeted, acquaintanceand distance strategies. In addition, we validate the efficacy of our methodwith diffusion data from a real-world online social system, Twitter. We findthat our method can detect more spreading topics in practice. Our approachprovides a new direction in spreading detection and should be useful fordesigning effective detection methods.

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