Application of the ant colony optimization algorithm to competitive viral marketing

Wan Shiou Yang, Shi Xin Weng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Consumers often form complex social networks based on a multitude of different relations and interactions. By virtue of these interactions, they influence each other's decisions in adopting products or behaviors. Therefore, it is essential for companies to identify influential consumers to target, in the hopes that influencing them will lead to a large cascade of further recommendations. Several studies, based on approximation algorithms and assume that the objective function is monotonic and submodular, have been addressed this issue of viral marketing. However, there is a complex and broad family of diffusion models in competitive environment, and the properties of monotonic and submodular may not be upheld. Therefore, in this research, we borrowed from swarm intelligence-specifically the ant colony optimization algorithm-to address the competitive influence-maximization problem. The proposed approaches were evaluated using a coauthorship data set from the arXiv e-print (http://www.arxiv.org), and the obtained experimental results demonstrated that our approaches outperform two well-known benchmark heuristics.

Original languageEnglish
Title of host publicationArtificial Intelligence
Subtitle of host publicationTheories and Applications - 7th Hellenic Conference on AI, SETN 2012, Proceedings
Pages1-8
Number of pages8
DOIs
Publication statusPublished - 2012 Jun 5
Event7th Hellenic Conference on Artificial Intelligence, SETN 2012 - Lamia, Greece
Duration: 2012 May 282012 May 31

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7297 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other7th Hellenic Conference on Artificial Intelligence, SETN 2012
CountryGreece
CityLamia
Period12-05-2812-05-31

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Yang, W. S., & Weng, S. X. (2012). Application of the ant colony optimization algorithm to competitive viral marketing. In Artificial Intelligence: Theories and Applications - 7th Hellenic Conference on AI, SETN 2012, Proceedings (pp. 1-8). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7297 LNCS). https://doi.org/10.1007/978-3-642-30448-4_1