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Compact models for influential nodes identification problem in directed networks.

Cheng Jiang1, Xueyong Liu1, Jun Zhang1

  • 1School of Management Engineering, Capital University of Economics and Business, Beijing 100070, China.

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Summary
This summary is machine-generated.

This study addresses the influential nodes identification problem (INIP) in directed networks, proposing a new metric and a heuristic algorithm. The developed methods demonstrate superior accuracy and discrimination compared to existing approaches.

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Area of Science:

  • Complex Networks Analysis
  • Network Science
  • Graph Theory

Background:

  • The influential nodes identification problem (INIP) is crucial in complex networks.
  • Existing methods primarily focus on undirected networks, with limited research on directed networks.
  • Methods for undirected networks are not directly applicable to directed networks.

Purpose of the Study:

  • To investigate the INIP specifically within directed networks.
  • To develop a novel metric for assessing node influence in directed networks.
  • To formulate and solve the INIP in directed networks efficiently.

Main Methods:

  • Proposal of a novel metric to quantify node influence in directed networks.
  • Formulation of a compact model for INIP, proven to be NP-Complete.
  • Design of a heuristic algorithm integrating a 2-opt local search within a greedy framework.

Main Results:

  • The proposed metric effectively assesses node influence in directed networks.
  • The heuristic algorithm provides accurate and discriminative identification of influential nodes.
  • Experimental results indicate outperformance over traditional measure-based heuristic methods.

Conclusions:

  • The study successfully addresses the INIP in directed networks.
  • The novel metric and heuristic algorithm offer significant improvements in accuracy and discrimination.
  • This work provides a valuable contribution to the field of complex network analysis.