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PDCSA: A parallel discrete crow search algorithm for influence maximization in social networks.

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This study introduces a parallel discrete crow search algorithm (PDCSA) to efficiently solve the influence maximization problem in large networks. PDCSA enhances computational speed while maintaining high performance for identifying key seed nodes.

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Area of Science:

  • Computer Science
  • Network Science
  • Artificial Intelligence

Background:

  • Influence maximization (IM) aims to find seed nodes for maximum network spread.
  • Conventional algorithms struggle with efficiency in large-scale networks.
  • Swarm intelligence algorithms show promise but require further efficiency improvements.

Purpose of the Study:

  • To propose an efficient algorithm for the influence maximization problem in large-scale networks.
  • To enhance the time efficiency of swarm intelligence-based algorithms for IM.
  • To leverage parallel computing for improved IM problem-solving.

Main Methods:

  • Development of a parallel discrete crow search algorithm (PDCSA).
  • Utilizing parallel computing for enhanced computational efficiency.
  • Algorithm designed based on the evolution characteristics of crow search.

Main Results:

  • PDCSA demonstrates comparable performance to state-of-the-art algorithms.
  • Achieved significant improvements in time efficiency for IM problems.
  • Experimental results on six datasets confirm high efficiency and robustness.

Conclusions:

  • PDCSA effectively addresses the efficiency challenges of IM in large networks.
  • The algorithm offers a robust and efficient solution for influence maximization.
  • Parallel computing integration significantly boosts the performance of swarm intelligence for IM.