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This study introduces novel evolutionary algorithms for Influence Maximization (IM) and Target Set Selection (TSS) in Boolean networks under the Deterministic Linear Threshold Model (DLTM). These hybrid algorithms significantly outperform existing methods on large-scale networks.

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Area of Science:

  • Computational Science
  • Network Science
  • Optimization

Background:

  • Influence Maximization (IM) and Target Set Selection (TSS) are critical problems in network analysis.
  • Existing methods for IM and TSS under the Deterministic Linear Threshold Model (DLTM) have limitations.
  • Boolean networks are widely used to model complex systems.

Purpose of the Study:

  • To reformulate IM and TSS problems for Boolean networks within the framework of pseudo-Boolean optimization.
  • To develop and evaluate novel hybrid algorithms combining evolutionary computation with greedy heuristics for these problems.
  • To propose a specialized (1+1)-Evolutionary Algorithm variant optimized for fixed Hamming weight subsets of the Boolean hypercube.

Main Methods:

  • Formulation of Influence Maximization and Target Set Selection as pseudo-Boolean optimization problems.
  • Development of a novel (1+1)-Evolutionary Algorithm variant for optimizing functions on Boolean hypercubes of fixed Hamming weight.
  • Hybridization of the proposed evolutionary algorithm with a greedy heuristic for initializing solutions in IM and TSS.

Main Results:

  • The proposed hybrid algorithms demonstrate significantly superior performance compared to combinations of greedy heuristics with classic (1+1)-Evolutionary Algorithms.
  • Experimental validation on real-world and random networks shows the effectiveness of the new algorithms.
  • The algorithms are scalable and applicable to large networks with tens of thousands of vertices.

Conclusions:

  • The novel hybrid evolutionary algorithms offer a more effective approach for solving Influence Maximization and Target Set Selection problems under the Deterministic Linear Threshold Model.
  • The specialized (1+1)-Evolutionary Algorithm variant is well-suited for Influence Maximization tasks.
  • The developed methods provide a powerful computational tool for analyzing large-scale Boolean networks.