IEEE Transactions on Computational Social Systems最新文献

IEEE Transactions on Computational Social Systems Information for Authors IEEE计算社会系统信息汇刊

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-04-01 Epub Date: 2026-04-02 DOI: 10.1109/TCSS.2026.3673050

引用次数: 0

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-04-01 Epub Date: 2026-04-02 DOI: 10.1109/TCSS.2026.3673048

引用次数: 0

Contrastive Token-Level Explanations for Graph-Based Rumor Detection 基于图的谣言检测的对比令牌层次解释

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-04-01 Epub Date: 2025-11-12 DOI: 10.1109/TCSS.2025.3622696

Daniel Wai Kit Chin;Roy Ka-Wei Lee

{"title":"Contrastive Token-Level Explanations for Graph-Based Rumor Detection","authors":"Daniel Wai Kit Chin;Roy Ka-Wei Lee","doi":"10.1109/TCSS.2025.3622696","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3622696","url":null,"abstract":"The widespread use of social media has accelerated the dissemination of information, but it has also facilitated the spread of harmful rumors, which can disrupt economies, influence political outcomes, and exacerbate public health crises, such as the COVID-19 pandemic. While graph neural network (GNN)-based approaches have shown significant promise in automated rumor detection, they often lack transparency, making their predictions difficult to interpret. Existing graph explainability techniques fall short in addressing the unique challenges posed by the dependencies among feature dimensions in high-dimensional text embeddings used in GNN-based models. In this article, we introduce contrastive token layerwise relevance propagation (CT-LRP), a novel framework designed to enhance the explainability of GNN-based rumor detection. CT-LRP extends current graph explainability methods by providing token-level explanations that offer greater granularity and interpretability. We evaluate the effectiveness of CT-LRP across multiple GNN models trained on three publicly available rumor detection datasets, demonstrating that it consistently produces high-fidelity, meaningful explanations, paving the way for more robust and trustworthy rumor detection systems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 2","pages":"2678-2688"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Preassigned-Time Distributed Optimization Protocol for Resource Allocation via a Novel Convergence Theorem 基于新收敛定理的资源分配预分配时间分布式优化协议

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-04-01 Epub Date: 2025-12-08 DOI: 10.1109/TCSS.2025.3629146

Zengyun Wang;Zuowei Cai;Zhenyuan Guo;Yang Cao;Xuegang Tan

{"title":"A Preassigned-Time Distributed Optimization Protocol for Resource Allocation via a Novel Convergence Theorem","authors":"Zengyun Wang;Zuowei Cai;Zhenyuan Guo;Yang Cao;Xuegang Tan","doi":"10.1109/TCSS.2025.3629146","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3629146","url":null,"abstract":"This manuscript introduces a novel preassigned-time distributed optimization protocol for resource allocation in multiagent systems, addressing both local convex set constraints and global equality constraints. The protocol operates through two sequential phases: initially, each agent’s state is deterministically driven into its feasible set within a prespecified time; subsequently, global equality constraints are continuously maintained while progressively converging to the optimal solution, achieving exact optimization within the total preassigned time. Distinct from conventional finite-time, fixed-time, or predefined-time distributed algorithms, our framework innovatively employs a state-based generator mechanism. A key advantage is the settling time’s invariance to both initial conditions and system parameters, enabling precise offline determination of convergence timelines and offering substantial practical benefits for real-time implementations. Numerical experiments validate the theoretical soundness and practical efficacy of the proposed methodology in resource-constrained distributed coordination scenarios.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 2","pages":"2615-2625"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665387","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SEmotion: Knowledge-Guided Sigmoid-Constrained Network for EEG-Based Emotion Recognition 情感：基于脑电图的情感识别的知识引导的s形约束网络

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-04-01 Epub Date: 2025-11-26 DOI: 10.1109/TCSS.2025.3625747

Wenjie Rao;Sheng-hua Zhong;Zhi Zhang;Yan Liu

{"title":"SEmotion: Knowledge-Guided Sigmoid-Constrained Network for EEG-Based Emotion Recognition","authors":"Wenjie Rao;Sheng-hua Zhong;Zhi Zhang;Yan Liu","doi":"10.1109/TCSS.2025.3625747","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3625747","url":null,"abstract":"Electroencephalography (EEG)-based affective computing has achieved significant progress due to the rapidly developing learning model. However, compared to computer vision or natural language processing-related tasks, EEG data presents challenges such as a low signal-to-noise ratio, small sample size, and nonstationary properties. These factors may affect the model’s performance on cross-domain tasks, suggesting that further improvements are necessary. Existing models aim to minimize intraclass distance and maximize interclass distance to achieve an optimal solution. This can potentially have a negative impact on the model’s generalization ability because they ignore the existence of low-quality training samples that may not be suitable for strict optimization. This article presents a method for determining sample quality based on the guidance of knowledge from emotional neuroscience. The method differentiates between high- and low-quality training samples and designs a corresponding loss function to impose intraclass and interclass constraints on hyperspherical manifolds based on the quality of the samples. Therefore, it can be argued that our proposed method achieves a better balance between reducing the intraclass distance of high-quality samples and preventing the overfitting of low-quality ones. This could potentially help build a more robust EEG emotion recognition model. Numerous experiments are conducted on the SJTU Emotion EEG Dataset (SEED) and SJTU Emotion EEG Dataset (IV) (SEED-IV) datasets under cross-subject and cross-session scenarios, which show the superior performance of our proposed method and its higher recognition accuracy against adversarial attacks.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 2","pages":"2689-2700"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy-Based Deep Reinforcement Learning for Suicidal Ideation Detection in Online Social Networks 基于模糊深度强化学习的在线社交网络自杀意念检测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-04-01 Epub Date: 2025-11-18 DOI: 10.1109/TCSS.2025.3622536

Greeshma Lingam;Sajal K. Das

{"title":"Fuzzy-Based Deep Reinforcement Learning for Suicidal Ideation Detection in Online Social Networks","authors":"Greeshma Lingam;Sajal K. Das","doi":"10.1109/TCSS.2025.3622536","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3622536","url":null,"abstract":"Suicidal ideation is a major psychological problem, and preventing this social risk is recognized as an important research topic. In reality, there can be several reasons why a person experiences suicidal ideation. Each individual can express views, emotions, and several types of symptoms related to suicidal ideation on the most popular social media platforms. In online social networks (OSNs), identification of suicidal ideation is one of the major challenging tasks. Existing studies have shown that the delay in understanding and identifying various risk factors can cause the suicidal event to occur. Due to the scarcity of data and understanding, the genuine intentions of people in their posts are the major challenges to improve the efficacy of suicidal ideation detection. Motivated by the existing psychological research, this article first analyzes an individual’s social behavior from different perspectives, namely, stress-oriented knowledge, tweet behavior, emotion transition sequence, social interaction, and other psychological factors. Next, a <italic>tweet inspection framework based on fuzzy deep reinforcement learning (FDRL) model is proposed to detect users with suicidal ideation in OSNs. In addition, a suicidal influential user is proposed by considering a suicidal influence minimization with minimum contextual modification model (SIM-MCM), which reduces the impact of suicidal influence without major changes in the contextual information during the cascading process in OSNs. Experimental results illustrate that the proposed model effectively detects users with suicidal ideation when compared with other deep learning classifier models.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 2","pages":"2661-2677"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Survey on Opinion Dynamics in Social Media Networks: Analysis, Simulation, and Control 社会媒体网络中意见动态的调查：分析、模拟与控制

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-04-01 Epub Date: 2025-12-11 DOI: 10.1109/TCSS.2025.3622498

Mohamed Zareer;Rastko R. Selmic

{"title":"A Survey on Opinion Dynamics in Social Media Networks: Analysis, Simulation, and Control","authors":"Mohamed Zareer;Rastko R. Selmic","doi":"10.1109/TCSS.2025.3622498","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3622498","url":null,"abstract":"The rapid proliferation of social networks has revolutionized communication and social interactions, rendering the study of opinion dynamics (OD) within these platforms an essential area of research. OD offers a powerful lens for understanding, simulating, and predicting behavioral patterns and interactions among individuals on social media networks. We conducted an advanced and comprehensive review examining the methodologies and tools used in this domain, focusing on agent-based modeling, network topology, dynamic modeling, and behavioral modeling within multiagent systems (MASs). Key challenges, such as computational complexity, data quality, and model validation, and potential strategies to overcome these limitations are discussed. The review also highlights critical trends and interdisciplinary opportunities, highlighting the integration of emerging technologies and the importance of ethical considerations in research. By studying advancements in simulation, analysis, and prediction in social media networks through OD, this work provides a comprehensive resource for researchers and practitioners to deepen their understanding and develop impactful applications in this field.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 2","pages":"2626-2660"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147665439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TRIDENT: Temporal Reasoning and Detection of Emerging Network Threats 三叉戟：新出现的网络威胁的时间推理和检测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-04-01 Epub Date: 2025-11-25 DOI: 10.1109/TCSS.2025.3621180

Ghadah Almousa;Yugyung Lee

{"title":"TRIDENT: Temporal Reasoning and Detection of Emerging Network Threats","authors":"Ghadah Almousa;Yugyung Lee","doi":"10.1109/TCSS.2025.3621180","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3621180","url":null,"abstract":"The rapid spread of misinformation on social media threatens public discourse, trust, and decision-making. Existing rumor detection methods—ranging from multimodal fusion to graph-based reasoning—have achieved promising results but often struggle to capture the multiview, heterogeneous, evolving, and adversarial nature of real-world rumor propagation. Common limitations include oversimplified graph structures, shallow temporal modeling (TM), and limited semantic grounding or robustness. To address these challenges, we propose temporal reasoning and detection of emerging network threats (TRIDENT), a unified and interpretable framework that represents rumor events as multiview heterogeneous temporal graphs enriched with external knowledge. TRIDENT integrates a novel partial differential equation–graph convolutional network (PDE-GCN) module for continuous-time temporal encoding, cross-view attention for joint reasoning over user interactions, content semantics, and propagation structure, and multimodal adversarial training to defend against label-preserving manipulations in both text and graph domains. Comprehensive evaluations on four benchmark datasets, <italic>Twitter15, <italic>Twitter16, <italic>Weibo, and <italic>PHEME, show that TRIDENT consistently outperforms state-of-the-art baselines, with especially strong gains on challenging rumor types such as <italic>false and <italic>unverified claims. These results highlight the importance of unifying TM, semantic grounding, and adaptive multiview graph reasoning for robust and explainable rumor detection in dynamic social ecosystems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 2","pages":"2701-2717"},"PeriodicalIF":4.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147588185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Systems, Man, and Cybernetics Society Information IEEE系统、人与控制论学会信息

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-02-13 DOI: 10.1109/TCSS.2026.3655848

引用次数: 0

IEEE Transactions on Computational Social Systems Information for Authors IEEE计算社会系统信息汇刊

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-02-13 DOI: 10.1109/TCSS.2026.3655850

引用次数: 0