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IEEE Technology Navigator

IEEE Technical Community Spotlight

Transactions on Computational Social Systems

Scope

IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. “Systems” include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.

Learn more and submit your paper today.

Posting Preprints to Non-IEEE Servers

Authors should disclose postings on approved preprint services when submitting papers to SMCS Journals.

Consult relevant sections posted here.

The following statement must be included on the initial screen of the preprint site:

  • “This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.”

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Editor-in-Chief

Bin Hu
Bin Hu

Editor-In-Chief

Gansu Provincial Key Laboratory of Wearable Computing

School of Information Science and Engineering, Lanzhou University

Lanzhou, China

Email: [email protected]

REQUEST TO UPDATE CONTENT BOOKMARK PAGE


 

Peer Review

The articles in this journal are peer reviewed in accordance with the requirements set forth in the IEEE Publication Services and Products Board Operations Manual. Each published article was reviewed by a minimum of two independent reviewers using a single-anonymous peer review process, where the identities of the reviewers are not known to the authors, but the reviewers know the identities of the authors. Articles will be screened for plagiarism before acceptance.

Editorial Board Members

  • HUSSEIN ABBASS, Univ. of New South Wales, Australia
  • MAINAK ADHIKARI, Indian Inst. of Science Education and Research, India
  • MOUSA AL-KFAIRY, Zayed Univ., UAE
  • KRISHAN BERWAL, Military College of Telecommunication Engineering, India
  • DANIEL BILAR, Norwich Univ., United States
  • HANSHU CAI, Lanzhou Univ., China
  • WEI CAI, Univ. of Washington, United States
  • ZHIPENG CAI, Georgia State Univ., United States
  • GABRIELLA CASALINO, Univ. of Bari Aldo Moro, Italy
  • LONG CHEN, WAYTOUS, China
  • XUEQI CHENG, Chinese Academy of Sci., China
  • ZHIYONG CHENG, ZhongJuYuan Intelligent Tech. Co., Ltd., China
  • CHINMAY CHAKRABORTY, Kalinga Inst. of Industrial Tech., India
  • PETER CHIN, Boston Univ., United States
  • MATTEO CINELLI, Sapienza Univ. of Rome, Italy
  • GEORGE CYBENKO, Dartmouth College, United States
  • GAURAV DHIMAN, Jagat Guru Nanak Dev Punjab State Open Univ., India
  • YOUCEF DJENOURI, Univ. of South-Eastern, Norway
  • QUNXI DONG, Beijing Inst. of Tech., China
  • WEI DUAN, National Univ. of Defense Tech., China
  • MARIA PIA FANTI, Politecnico di Bari, Italy
  • LUOYI FU, Shanghai Jiao Tong Univ., China
  • MARINA L. GAVRILOVA, Univ. of Calgary, Canada
  • HANSU GU, Amazon.com Inc., United States
  • SAQIB HAKAK, Univ. of New Brunswick, Canada
  • RICHANG HONG, Hefei Univ. of Tech., China
  • XIPING HU, Bravolol Ltd., China
  • ROY KA-WEI LEE, Singapore Univ. of Tech. and Design, Singapore
  • VICTOR C.M. LEUNG, Univ. of British Colombia, Canada
  • JUANJUAN LI, Beijing Inst. of Tech., China
  • KEN LI, Univ. College London, United Kingdom
  • MI LI, Beijing Univ. of Tech. China
  • QING LI, The Hong Kong Polytechnic Univ., China
  • XUERONG LI, Chinese Acad. of Sci., China
  • GUANJUN LIU, Tongji Univ. China
  • XIAOFAN LIU, City Univ. of Hong Kong, China
  • YU LIU, South China Univ. of Tech., China
  • ZHE LIU, Zhejiang Lab, China
  • ZHIHAN LV, Uppsala Univ., Sweden
  • KENJI MORITA, The Univ. of Tokyo, Japan
  • SENTHIL MURUGAN NAGARAJAN, Univ. of Luxembourg, Luxembourg
  • NURIA OLIVER, Vodafone, United Kingdom
  • VIR PHOHA, Syracuse Univ., United States
  • RUI QIN, Chinese Academy of Sciences, China
  • RAJA. S.P., VelTech Univ., India
  • SRIPARNA SAHA, Indian Inst. of Tech. Patna, India
  • YINGYING SHE, Xiamen Univ., China
  • KAIZE SHI, Univ. of Tech. Sydney, Australia
  • AMIT KUMAR SINGH, Nat. Inst. of Tech. Patna, India
  • KRISHNA KANT SINGH, Delhi Technical Campus of GGSIPU, India
  • DAWEI SONG, The Open Univ. (OU), United Kingdom
  • GAUTAM SRIVASTAVA, Brandon Univ., Canada
  • YING TANG, Rowan Univ., United States
  • ZHE WAN, National Development and Reform Commission, China
  • FEI-YUE WANG, Chinese Academy of Sci., China
  • SHIQIANG WANG, Thomas J. Watson Research Center, United States
  • TAO WANG, National Univ. of Defense Tech., China
  • XIAO WANG, Qingdao Academy of Intelligent Industries, China
  • ZHIYUAN WEN, Nanjing Tech. and Innovation Research Inst., China
  • KEVIN XU, Univ. Toledo, United States
  • DONGREN YAO, Harvard Medical School, United States
  • PEIJUN YE, Chinese Academy of Sci., China
  • HUI YU, Univ. of Glasgow, United Kingdom
  • LIYING ZHANG, Central Univ. of Finance and Economics, China
  • QINGPENG ZHANG, Univ. of Hong Kong, China
  • TONG ZHANG, South China Univ. of Tech., China
  • MIN ZHOU, Beijing Jiaotong Univ., China
  • XIAOKANG ZHOU, Kansai Univ., Japan
  • HAIBIN ZHU, Nipissing Univ., Canada
  • JIA ZHU, Zhejiang Normal Univ., China
  • JIANMING ZHU, Univ. of Chinese Academy of Sci., China

 

Articles

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CURRENT ISSUE
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06 October 2025
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31 July 2025
Generation and Detection of Sign Language Deepfakes: A Linguistic and Visual Analysis
This research explores the positive application of deepfake technology for upper body generation, specifically sign language for the D(d)eaf and hard of hearing (DHoH) community. Given the complexity of sign language and the scarcity of experts, the generated videos are vetted by a sign language expert for accuracy. We construct...
08 July 2025
Coordinate System Transformation Method for Comparing Different Types of Data in Different Dataset Using Singular Value Decomposition
In the current era of AI technology, where systems increasingly rely on big data to process vast amounts of societal information, efficient methods for integrating and utilizing diverse datasets are essential. This article presents a novel approach for transforming the feature space of different datasets through singular value decomposition (SVD)...
30 April 2025
An EEG-Based Positive Feedback Mechanism for VR Mindfulness Meditation to Improve Emotion Regulation
Virtual reality (VR) mindfulness meditation has emerged as a prominent emotion regulation strategy in recent years. Current research often seeks to enhance meditation effectiveness through biofeedback and overlooks the trajectory of emotional changes and the changing needs during regulation. In this study, we propose an electroencephalography (EEG)–based positive feedback mechanism...
16 April 2025
LLM-Driven Cognitive Modeling for Personalized Travel Generation
Traditional cognitive travel modeling typically employs a unified cognitive model to simulate representative travel behaviors, which may usually result in a weak characterization of user heterogeneity in paths, modes, and other factors. Large language model (LLM), by contrast, has significantly enhanced the anthropomorphic and personalized features of intelligent systems. To...
18 March 2025
Temporal Link Prediction in Social Networks Based on Agent Behavior Synchrony and a Cognitive Mechanism
Temporality, a crucial characteristic in the formation of social relationships, was used to quantify the long-term time effects of networks for link prediction models, ignoring the heterogeneity of time effects on different time scales. In this work, we propose a novel approach to link prediction in temporal networks, extending existing...

See more at IEEE Xplore

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