Högskolan i Skövde

his.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A fuzzy logic approach to influence maximization in social networks
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Distribuerade realtidssystem (DRTS), Distributed Real-Time Systems)ORCID iD: 0000-0002-7312-9089
College of Information Technology, United Arab Emirates University, Al-Ain, United Arab Emirates.
Royal Institute of Management, Thimphu, Bhutan.
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Distribuerade realtidssystem (DRTS), Distributed Real-Time Systems)ORCID iD: 0000-0002-1039-5830
2020 (English)In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 11, no 6, p. 2435-2451Article in journal (Refereed) Published
Abstract [en]

Within a community, social relationships are paramount to profile individuals’ conduct. For instance, an individual within a social network might be compelled to embrace a behaviour that his/her companion has recently adopted. Such social attitude is labelled social influence, which assesses the extent by which an individual’s social neighbourhood adopt that individual’s behaviour. We suggest an original approach to influence maximization using a fuzzy-logic based model, which combines influence-weights associated with historical logs of the social network users, and their favourable location in the network. Our approach uses a two-phases process to maximise influence diffusion. First, we harness the complexity of the problem by partitioning the network into significantly-enriched community-structures, which we then use as modules to locate the most influential nodes across the entire network. These key users are determined relatively to a fuzzy-logic based technique that identifies the most influential users, out of which the seed-set candidates to diffuse a behaviour or an innovation are extracted following the allocated budget for the influence campaign. This way to deal with influence propagation in social networks, is different from previous models, which do not compare structural and behavioural attributes among members of the network. The performance results show the validity of the proposed partitioning-approach of a social network into communities, and its contribution to “activate” a higher number of nodes overall. Our experimental study involves both empirical and real contemporary social-networks, whereby a smaller seed set of key users, is shown to scale influence to the high-end compared to some renowned techniques, which employ a larger seed set of key users and yet they influence less nodes in the social network.

Place, publisher, year, edition, pages
Springer, 2020. Vol. 11, no 6, p. 2435-2451
Keywords [en]
Social networks, Community detection, Influence propagation, Fuzzy logic
National Category
Computer and Information Sciences
Research subject
Distributed Real-Time Systems
Identifiers
URN: urn:nbn:se:his:diva-16779DOI: 10.1007/s12652-019-01286-2ISI: 000536462400019Scopus ID: 2-s2.0-85064252809OAI: oai:DiVA.org:his-16779DiVA, id: diva2:1305165
Available from: 2019-04-15 Created: 2019-04-15 Last updated: 2020-06-29Bibliographically approved

Open Access in DiVA

fulltext(3132 kB)227 downloads
File information
File name FULLTEXT02.pdfFile size 3132 kBChecksum SHA-512
94982f0082f90511b53d40cf5757a746e22d2752e2332781b5fa4a51fdddea8dace38a68c50b2297b53f225297758cbb9e7c68c8a1286a17cb9de3951eb05bcc
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopus

Authority records

Atif, YacineLindström, Birgitta

Search in DiVA

By author/editor
Atif, YacineLindström, Birgitta
By organisation
School of InformaticsInformatics Research Environment
In the same journal
Journal of Ambient Intelligence and Humanized Computing
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 353 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 1372 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • apa-cv
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf