Influencers identification in complex networks through reaction-diffusion dynamics

TL;DR

This paper introduces a novel centrality metric based on reaction-diffusion dynamics and effective distance, significantly improving the identification of influencers in complex networks for contagion processes.

Contribution

The paper proposes a new influencer identification method using effective distance, outperforming existing centrality metrics across various network topologies.

Findings

New centrality metric outperforms state-of-the-art methods

Effective distance is crucial for influencer detection

Method improves contagion process predictions

Abstract

A pivotal idea in network science, marketing research and innovation diffusion theories is that a small group of nodes -- called influencers -- have the largest impact on social contagion and epidemic processes in networks. Despite the long-standing interest in the influencers identification problem in socio-economic and biological networks, there is not yet agreement on which is the best identification strategy. State-of-the-art strategies are typically based either on heuristic centrality metrics or on analytic arguments that only hold for specific network topologies or peculiar dynamical regimes. Here, we leverage the recently introduced random-walk effective distance -- a topological metric that estimates almost perfectly the arrival time of diffusive spreading processes on networks -- to introduce a new centrality metric which quantifies how close a node is to the other nodes. We show that the new centrality metric significantly outperforms state-of-the-art metrics in detecting the influencers for global contagion processes. Our findings reveal the essential role of the network effective distance for the influencers identification and lead us closer to the optimal solution of the problem.