An evolutionary framework for maximizing influence propagation in social networks

Giovanni Iacca*, Kateryna Konotopska, Doina Bucur, Alberto Tonda

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
26 Downloads (Pure)

Abstract

Social networks are one the main sources of information transmission nowadays. However, not all nodes in social networks are equal: in fact, some nodes are more influential than others, i.e., their information tends to spread more. Finding the most influential nodes in a network – the so-called Influence Maximization problem – is an NP-hard problem with great social and economical implications. Here, we introduce a framework based on Evolutionary Algorithms that includes various graph-aware techniques (spread approximations, domain-specific operators, and node filtering) that facilitate the optimization process. The framework can be applied straightforwardly to various social network datasets, e.g., those in the SNAP repository.

Original languageEnglish
Article number100107
JournalSoftware Impacts
Volume9
Early online date24 Jul 2021
DOIs
Publication statusPublished - Aug 2021

Keywords

  • Evolutionary algorithm
  • Influence maximization
  • Social network

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