Not logged in.

Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Influencers identification in complex networks through reaction-diffusion dynamics
Organization Unit
  • Flavio Iannelli
  • Manuel Mariani
  • Igor M Sokolov
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title Physical review. E
Publisher American Physical Society
Geographical Reach international
ISSN 2470-0045
Volume 98
Page Range 062302
Date 2018
Abstract Text 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.
Digital Object Identifier 10.1103/PhysRevE.98.062302
Other Identification Number merlin-id:17047
PDF File Download from ZORA
Export BibTeX