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Identifying influential spreaders in complex networks by propagation probability dynamics.

Duan-Bing Chen1, Hong-Liang Sun2, Qing Tang3

  • 1Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China.

Chaos (Woodbury, N.Y.)
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Identifying critical nodes in complex networks is key for understanding spreading phenomena. A new method, DynamicRank, effectively ranks these vital nodes by considering their neighbors' influence, outperforming existing approaches.

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Area of Science:

  • Complex Systems Science
  • Network Science
  • Computational Social Science

Background:

  • Spreading and cascading processes in complex systems are often dominated by a few critical nodes.
  • Identifying these influential nodes is crucial for understanding and controlling network dynamics.

Purpose of the Study:

  • To propose a novel method, DynamicRank, for identifying vital nodes in complex networks based on propagation dynamics.
  • To evaluate the effectiveness of DynamicRank compared to existing node centrality measures.

Main Methods:

  • DynamicRank calculates node influence by summing probability scores of its i-order neighbors.
  • The method was tested using Susceptible-Infected-Recovered (SIR) and Susceptible-Infected-Susceptible (SIS) models on real-world networks.

Main Results:

  • DynamicRank significantly outperforms established methods like Coreness, H-index, LocalRank, Betweenness, and Spreading Probability, as measured by the Kendall τ coefficient.
  • The method exhibits linear time complexity, allowing efficient application to large-scale networks.

Conclusions:

  • DynamicRank offers a novel and effective approach for identifying vital nodes in complex networks.
  • Its computational efficiency and superior performance make it suitable for analyzing large, real-world network structures and propagation dynamics.