
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 againJournal information
- Editor-in-Chief
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- Reda Alhajj
- Publishing model
- Hybrid (Transformative Journal). Learn about publishing Open Access with us
Journal metrics
- 99 days
- Submission to first decision
- 222 days
- Submission to acceptance
- 117,961 (2020)
- Downloads
Latest issue
Latest articles
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Creative social media use for Covid-19 prevention in Bangladesh: a structural equation modeling approach
Authors (first, second and last of 4)
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Journal updates
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Special Issue on Complex Networks & Applications
This special issue presents a wide range of complex networks theoretical works and applications focused on social environments such as Information Spreading in Social Media, Rumor and Viral Marketing, Quantifying Success through Social Network Analysis, Human behavior & mobility, Social Reputation, Influence, and Trust.
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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.
About this journal
- Electronic ISSN
- 1869-5469
- Print ISSN
- 1869-5450
- Abstracted and indexed in
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- ACM Digital Library
- CNKI
- DBLP
- Dimensions
- EBSCO Discovery Service
- EI Compendex
- Emerging Sources Citation Index
- Google Scholar
- INSPEC
- Institute of Scientific and Technical Information of China
- Japanese Science and Technology Agency (JST)
- Naver
- OCLC WorldCat Discovery Service
- ProQuest Advanced Technologies & Aerospace Database
- ProQuest Central
- ProQuest SciTech Premium Collection
- ProQuest Technology Collection
- ProQuest-ExLibris Primo
- ProQuest-ExLibris Summon
- SCImago
- SCOPUS
- TD Net Discovery Service
- UGC-CARE List (India)
- WTI Frankfurt eG
- Copyright information