期刊信息

IEEE Transactions on Computational Social Systems (TCSS)

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影响因子:
4.9
出版商:
IEEE
ISSN:
2373-7476
浏览:
31769
关注:
15

征稿

IEEE Transactions on Computational Social Systems (TCSS) is an academic journal published by IEEE. (ISSN 2373-7476, impact factor 4.9, CCF C).

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.
由 Dou Sun 最后更新于

特刊

特刊：Cyber-Physical-Social Intelligence: State-of-the-art, Perspectives, and Challenges 截稿日期: 2026-11-10 Introduction Cyber-Physical-Social Systems (CPSS) combine cyber, physical, and social spaces, enabling systems that simultaneously perceive, interpret, and interact with the physical environment and human behaviors. Through this deep cross-domain integration, CPSS are increasingly capable of context-aware monitoring, adaptive control, personalized assistance, and collaborative decision support. The shift from perception to cognition and from basic interconnection to intelligent coordination motivates the development of Cyber-Physical-Social Intelligence (CPSI), which aims to provide users with the ability to comprehend complex scenarios, anticipate future dynamics, and make autonomous decisions. Recent advances in CPSI have rapidly transitioned from conventional rule-based or domain-specific methods to more integrated forms of cognition and autonomy. Despite rapid progress in multimodal fusion, holistic awareness, and distributed intelligence, CPSI still faces several fundamental challenges in model construction, intelligent knowledge discovery, decision-making, high-performance computing, and system security. These challenges include accurately capturing coupled cyber-physical-social dynamics, ensuring robust multimodal representation and cross-domain alignment, modeling cognitive behaviors under uncertainty, and maintaining real-time digital-twin synchronization. Knowledge discovery remains difficult due to heterogeneous and incomplete data, ambiguous human intentions, and the need for reliable semantic reasoning, causal inference, and emergent behavior detection. Intelligent decision-making is hindered by the difficulty of learning generalizable policies, coordinating multi-agent planning, predicting long-term behavioral tendencies, and translating high-level reasoning into dependable actions. Scalable real-time CPSI is constrained by the computational demands of perception and foundation models, high-frequency synchronization, and distributed learning under strict resource limitations. Moreover, achieving secure and trustworthy CPSI is complicated by expanded cross-domain attack surfaces, privacy risks, vulnerabilities in generative models, and the need for transparent, resilient intelligence across cyber-physical-social ecosystems. This Special Issue invites high-quality original contributions from researchers and practitioners to advance theoretical and practical approaches in CPSI, enhancing cognitive reasoning, behavioral prediction, cross-domain perception, autonomous adaptation, human-AI collaboration, and system resilience, to push the boundaries toward truly intelligent, trustworthy, and self-evolving CPSS. Topics include but are not limited to: AI/ML-driven sensing and perception for cyber, physical, and social environments Cognitive behavior modeling and human-machine interaction in CPSI Digital twin modeling and real-time synchronization in CPSS Deep multimodal fusion architectures for heterogeneous CPSS data Embodied intelligence and CPSI-driven agents for adaptive interaction and decision-making LLM-based semantic reasoning, cross-domain alignment, and causal inference Reinforcement learning and optimal control for adaptive CPSS behavior Generative AI-based modeling, simulation, and prediction of CPSS dynamics Agent-based simulation of social interaction and collective intelligence Cognitive situation awareness and intent prediction in dynamic CPSI environments LLM-driven multi-agent systems for planning and collective decision-making CPSI for metaverse governance, safety, and trustworthy digital ecosystems Intelligent defense and resilience against cyber-physical-social attacks Federated learning and privacy-preserving techniques for large-scale CPSS Explainable, transparent, and trustworthy AI models for CPSI decision-making Data-driven intelligent services in CPSS Important Dates: Paper Submission Deadline: November 10, 2026 First Decision Deadline: January 15, 2027 Revision Deadline: February 15, 2027 Final Decision Deadline: March 15, 2027 Submission Guidelines: Authors should prepare their manuscripts according to the submission guidelines of the IEEE Transactions on Computational Social Systems. Manuscripts should be submitted through the online submission system at: https://ieee.atyponrex.com/journal/tcss, and select “Special Issue” of “Cyber-Physical-Social Intelligence: State-of-Art, Perspectives, and Challenges” under the Manuscript Category. All submissions will undergo a rigorous, single-blind peer-review process. Guest Editors: Xiaokang Wang, Zhengzhou University, China Parimala Thulasiraman, University of Manitoba, Canada Sin G. Teo, A*STAR, Singapore Xueqin Liang, Xidian University, China
由 Dou Sun 最后更新于

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CCF全称影响因子出版商ISSN
BIEEE Transactions on Neural Networks and Learning Systems8.9IEEE1045-9227
BNeural Networks7.2Elsevier0893-6080

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