Influencers identification in complex networks through reaction-diffusion dynamics
Autor: | Manuel Sebastian Mariani, Igor M. Sokolov, Flavio Iannelli |
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Přispěvatelé: | University of Zurich |
Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
FOS: Computer and information sciences
Physics - Physics and Society Theoretical computer science 3104 Condensed Matter Physics Computer science FOS: Physical sciences Network science UFSP13-1 Social Networks Physics and Society (physics.soc-ph) Network topology 01 natural sciences 010305 fluids & plasmas 10004 Department of Business Administration 0103 physical sciences 3109 Statistical and Nonlinear Physics 2613 Statistics and Probability 010306 general physics Social and Information Networks (cs.SI) Computer Science - Social and Information Networks Complex network Influencer marketing 330 Economics Identification (information) Metric (mathematics) Centrality Biological network |
Popis: | 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. |
Databáze: | OpenAIRE |
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