Journal article

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Influencers identification in complex networks through reaction-diffusion dynamics

  • Iannelli, Flavio Institute for Physics, Humboldt-University of Berlin, Germany
  • Mariani, Manuel Sebastian Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China - URPP Social Networks, Universität Zürich, Switzerland - Department of Physics, University of Fribourg, Switzerland
  • Sokolov, Igor M. Institute for Physics, Humboldt-University of Berlin, Germany
    03.12.2018
Published in:
  • Physical Review E. - 2018, vol. 98, no. 6, p. 062302
English 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 socioeconomic 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 measures 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 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.
Faculty
Faculté des sciences et de médecine
Department
Département de Physique
Language
  • English
Classification
Physics
License
License undefined
Identifiers
Persistent URL
https://folia.unifr.ch/unifr/documents/307473
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