Social Network Analysis and Mining (SNAM) is a multidisciplinary journal serving researchers and practitioners in academia and industry. It is the main venue for a wide range of researchers and readers from computer science, network science, social sciences, mathematical sciences, medical and biological sciences, financial, management and political sciences. We solicit experimental and theoretical work on social network analysis and mining using a wide range of techniques from social sciences, mathematics, statistics, physics, network science and computer science.

92% of authors who answered a survey reported that they would definitely publish or probably publish in the journal again

Journal information

Editor-in-Chief
  • Reda Alhajj
Publishing model
Hybrid. Learn about publishing OA with us

Journal metrics

87 days
Submission to first decision
210 days
Submission to acceptance
100,352 (2019)
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This journal has 34 open access articles

Journal updates

  • Call for Papers for Special Issue on Tackling COVID-19 Infodemic

    In this special issue, we provide an interdisciplinary forum for researchers and practitioners to combat the COVID-19 infodemic. We expect novel research to study the understanding, detection, mitigation, and measurement of the COVID-19 infodemic and potential future outbreaks. To facilitate further research in COVID-19 infodemic, this special issue  welcomes interdisciplinary research articles, new open access datasets, repositories, and benchmarks, broadening research on crisis informatics and its development. 

    New Content Item
  • Big Data Analytics and Deep Learning for Social Network Security

    Network analysis may play a considerable role in the new setup. However, security becomes a major issue when it comes to big data and network analysis in a distributed environment. Therefore, big data analysts, network security experts, and data scientists hold a prominent position in the current era, where data scientists are highly needed and there is a visible shortage in the market. This special issue highlights the challenges and solutions of Deep Learning and Network Security algorithms for Big Data, to improve the effectiveness for data security.

    New Content Item
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About this journal

Electronic ISSN
1869-5469
Print ISSN
1869-5450
Abstracted and indexed in
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  2. CNKI
  3. DBLP
  4. Dimensions
  5. EBSCO Discovery Service
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  10. Institute of Scientific and Technical Information of China
  11. Japanese Science and Technology Agency (JST)
  12. Naver
  13. OCLC WorldCat Discovery Service
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  16. ProQuest SciTech Premium Collection
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  21. SCOPUS
  22. TD Net Discovery Service
  23. UGC-CARE List (India)
  24. WTI Frankfurt eG
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