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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Influencers identification in complex networks through reaction-diffusion dynamics

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Author(s):
Iannelli, Flavio [1] ; Mariani, Manuel S. [2, 3, 4] ; Sokolov, Igor M. [1]
Total Authors: 3
Affiliation:
[1] Humboldt Univ, Inst Phys, Newtonstr 15, D-12489 Berlin - Germany
[2] Univ Fribourg, Dept Phys, CH-1700 Fribourg - Switzerland
[3] Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Sichuan - Peoples R China
[4] Univ Zurich, URPP Social Networks, CH-8050 Zurich - Switzerland
Total Affiliations: 4
Document type: Journal article
Source: Physical Review E; v. 98, n. 6 DEC 3 2018.
Web of Science Citations: 3
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 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. (AU)

FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
Grantee:Elbert Einstein Nehrer Macau
Support type: Research Projects - Thematic Grants