Social Network Analysis and Mining

Aims & Scope

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.

The main areas covered by SNAM include: (1) data mining advances on the discovery and analysis of communities, personalization for solitary activities (e.g. search) and social activities (e.g. discovery of potential friends), the analysis of user behavior in open forums (e.g. conventional sites, blogs and forums) and in commercial platforms (e.g. e-auctions), and the associated security and privacy-preservation challenges; (2) social network modeling, construction of scalable and customizable social network infrastructure, identification and discovery of complex, dynamics, growth, and evolution patterns using machine learning and data mining approaches or multi-agent based simulation; (3) social network analysis and mining for open source intelligence and homeland security.

Papers should elaborate on data mining and machine learning or related methods, issues associated to data preparation and pattern interpretation, both for conventional data (usage logs, query logs, document collections) and for multimedia data (pictures and their annotations, multi-channel usage data).

Topics include but are not limited to: Applications of social network in business engineering, scientific and medical domains, homeland security, terrorism and criminology, fraud detection, public sector, politics, and case studies.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.8
2024	2.30

SJR (SCImago Journal Rank)

Year	Value
2024	0.624

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	54

Journal Rank

Year	Value
2024	9273

Journal Citation Indicator

Year	Value
2024	2084

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, Engineering and Social Sciences, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• A review on sentiment analysis and emotion detection from text

  Citation: 566

  Authors: Pansy, Rupali

• Online engagement factors on Facebook brand pages

  Citation: 513

  Authors: Irena, Florian

• Social recommendation: a review

  Citation: 395

  Authors: Jiliang, Xia, Huan

• Social media and political communication: a social media analytics framework

  Citation: 387

  Authors: Stefan, Linh

• Study on centrality measures in social networks: a survey

  Citation: 340

  Authors: Kousik, Sovan, Madhumangal

• Social network analysis: developments, advances, and prospects

  Citation: 249

  Authors: John

• Topic modeling and sentiment analysis of global climate change tweets

  Citation: 228

  Authors: Biraj, Sathish A. P., Zhenlong

• Sentiment analysis on the impact of coronavirus in social life using the BERT model

  Citation: 202

  Authors: Mrityunjay, Amit Kumar, Shivam

• Deep learning for misinformation detection on online social networks: a survey and new perspectives

  Citation: 200

  Authors: Md Rafiqul, Shaowu, Xianzhi, Guandong

• Emotion detection from text and speech: a survey

  Citation: 187

  Authors: Kashfia, Manmeet, Jon, Reda

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