Combining greedy and evolutionary algorithms to maximize influence in networks under deterministic linear threshold model

Alexander Andreev; Stepan Kochemazov; Alexander Semenov

doi:10.1371/journal.pone.0331109

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Open Access

Peer-reviewed

Research Article

Combining greedy and evolutionary algorithms to maximize influence in networks under deterministic linear threshold model

Alexander Andreev,

Roles Investigation, Software, Visualization

Affiliation Information Technologies and Programming Faculty, ITMO University, Saint Petersburg, Russia
⨯
Stepan Kochemazov,

Roles Methodology, Validation, Writing – review & editing

Affiliation Laboratory of Logical and Optimization Methods for Analysis of Complex Systems, Matrosov Institute for System Dynamics and Control Theory SB RAS, Irkutsk, Russia

https://orcid.org/0000-0003-2848-5786

⨯
Alexander Semenov

Roles Conceptualization, Formal analysis, Methodology, Writing – original draft

* E-mail: biclop.rambler@yandex.ru

Affiliation Information Technologies and Programming Faculty, ITMO University, Saint Petersburg, Russia
⨯

Combining greedy and evolutionary algorithms to maximize influence in networks under deterministic linear threshold model

Alexander Andreev,
Stepan Kochemazov,
Alexander Semenov

Published: September 8, 2025
https://doi.org/10.1371/journal.pone.0331109

About the Authors

Alexander Andreev

Roles Investigation, Software, Visualization

Affiliation Information Technologies and Programming Faculty, ITMO University, Saint Petersburg, Russia

Stepan Kochemazov

Roles Methodology, Validation, Writing – review & editing

Affiliation Laboratory of Logical and Optimization Methods for Analysis of Complex Systems, Matrosov Institute for System Dynamics and Control Theory SB RAS, Irkutsk, Russia

https://orcid.org/0000-0003-2848-5786

Alexander Semenov

Roles Conceptualization, Formal analysis, Methodology, Writing – original draft

* E-mail: biclop.rambler@yandex.ru

Affiliation Information Technologies and Programming Faculty, ITMO University, Saint Petersburg, Russia

Competing Interests

The authors have declared that no competing interests exist.

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Algorithms
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Evolutionary algorithms
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Optimization
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Network analysis
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Neural networks
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Genetic algorithms
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Agent-based modeling
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Random variables
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