IEEE Transactions on Computational Social Systems最新文献_第7页

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

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 DOI: 10.1109/TCSS.2026.3730936

引用次数: 0

Human-Aware Social Navigation With Comfort Space for Mobile Robots in Human–Robot Coexistence Environments 人机共存环境下具有舒适空间的移动机器人人类感知社会导航

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2025-11-26 DOI: 10.1109/TCSS.2025.3627386

Ying Zhang;Wenming Guan;Guohui Tian;Cui-Hua Zhang;Guanglei Zhao;Changchun Hua

{"title":"Human-Aware Social Navigation With Comfort Space for Mobile Robots in Human–Robot Coexistence Environments","authors":"Ying Zhang;Wenming Guan;Guohui Tian;Cui-Hua Zhang;Guanglei Zhao;Changchun Hua","doi":"10.1109/TCSS.2025.3627386","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3627386","url":null,"abstract":"Human-aware social robot navigation, which achieves safe and comfortable navigation by generating human-friendly paths, is a crucial and challenging task for mobile robots deployed in human–robot coexistence environments. This article proposes a human-aware social navigation solution for mobile robots with the Gaussian mixture model (GMM)-based comfort spaces. Unlike existing methods, our proposed solution comprehensively considers both the movement and social communication states of individuals to achieve safe and comfortable human-aware social robot navigation while ensuring navigation efficiency. Specifically, for dynamic scenarios, we design a novel comfort space based on a six-component GMM to meet the comfort of individuals in motion. In addition, for communication scenarios, our solution can address a variety of interaction scenarios, such as two people without communication, two people face-to-face communication, discussions between two people in different positions and social group communication of more than two people. The validity and efficiency of the presented solution are fully verified in terms of planned paths, navigation efficiency, and comfort satisfaction through comparative experiments in both simulation and real-world environments. Meanwhile, a questionnaire survey with 241 participants also shows that our proposal is reasonable and feasible.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6798-6809"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097077","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

Occupational Inheritance and Kinship: An Empirical Study of Historical Social Elites on Wikidata 职业传承与亲缘关系：历史社会精英对维基数据的实证研究

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-01-21 DOI: 10.1109/TCSS.2026.3652200

Sirui Lai;Liang Wang;Dingqi Yang

{"title":"Occupational Inheritance and Kinship: An Empirical Study of Historical Social Elites on Wikidata","authors":"Sirui Lai;Liang Wang;Dingqi Yang","doi":"10.1109/TCSS.2026.3652200","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3652200","url":null,"abstract":"The relationship between occupational inheritance and kinship has often been witnessed in human history. Existing studies often rely on small-scale and regional data, and fail to reveal global and spatiotemporal dynamics of occupational inheritance. In this article, we conduct an empirical study on a large-scale dataset of historical social elites crawled from Wikidata, consisting of 691 503 validated relationship records involving 334 099 notable people from 3500 BCE to 2020 CE. Based on this dataset, we propose computational approaches to analyze the relationship between occupational inheritance and kinship, by defining the occupation connection index (OCI) measuring pairwise occupation connections under a certain social relationship, and the occupational inheritance index (OII) measuring the overall occupation inheritance across all occupation categories under parent–child relationships only. Subsequently, we reveal the varying impact of kinship over different occupation categories using OCI and also find interesting spatiotemporal patterns of OII reflecting key shifts in occupational inheritance which well align with key historical events and societal changes. Our study sheds light on the potential computational approaches to understanding occupational inheritance and kinship in a systematic manner with empirical evidence, which we believe will inspire further investigations in the field of computational social sciences.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6447-6458"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097140","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

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-02-18 DOI: 10.1109/TCSS.2026.3655190

Qihao Zhou;Weijie Yu;De Zhao;Wei Wang;Yangzhen Zhao;Zhilin Qian

{"title":"Trend Similarity-Enhanced Holiday Intercity Mobility Flow Prediction Under Historical Data Scarcity","authors":"Qihao Zhou;Weijie Yu;De Zhao;Wei Wang;Yangzhen Zhao;Zhilin Qian","doi":"10.1109/TCSS.2026.3655190","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3655190","url":null,"abstract":"Intercity mobility management plays a critical role in promoting urban sustainability and regional integration. Accurate prediction of mobility flows is essential for optimizing transport operations and enhancing resource allocation, especially during holidays. However, event-induced scarcity of reliable historical data makes it challenging for existing methods to capture the abrupt changes in holiday mobility patterns. To address these challenges, this article proposes the dynamic time-space holiday feature captor (DHC), which integrates spatiotemporal correlations in time-series data with cross-holiday similarities to effectively model the dynamic patterns of holiday mobility, thereby significantly improving prediction accuracy. The DHC model comprises four key modules: 1) vision dynamic extension (VDE); 2) spatiotemporal feature acquisition (SFA); 3) vision feature extraction (VFE); and 4) holiday feature fusion (HFF). The main experimental results show that DHC outperforms existing methods on multiple metrics, achieving reductions of 33.98%, 26.14%, and 35.75% in MAE, RMSE, and MAPE, respectively. Moreover, the model’s effectiveness and generalizability are further validated through ablation studies and additional experiments conducted across various holidays. This research advances mobility-flow prediction and management during holidays and contributes to the development of sustainable transportation systems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6403-6420"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097206","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

Adaptive Disinformation Diffusion on Networks With Heterogeneous Node and Link Dynamics 异构节点和链路动态网络的自适应虚假信息扩散

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-03-12 DOI: 10.1109/TCSS.2026.3686750

Alice Nanyanzi;Sridhar Radhakrishnan;Kash Barker;Venu Sirisanagandla;Weinan Wang

{"title":"Adaptive Disinformation Diffusion on Networks With Heterogeneous Node and Link Dynamics","authors":"Alice Nanyanzi;Sridhar Radhakrishnan;Kash Barker;Venu Sirisanagandla;Weinan Wang","doi":"10.1109/TCSS.2026.3686750","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3686750","url":null,"abstract":"This article introduces a novel adaptive diffusion model for disinformation propagation that captures temporally evolving, history dependent influence between node pairs. Unlike classical diffusion models, such as independent cascade, threshold, and compartmental models, which assume fixed or memoryless transmission probabilities, our approach assigns each directed link a time-varying influence strength <inline-formula> <tex-math>$theta_{ij}(t)$</tex-math> </inline-formula>. This parameter represents the dynamic influence strength from source <inline-formula> <tex-math>$i$</tex-math> </inline-formula> (disinformed node) to target <inline-formula> <tex-math>$j$</tex-math> </inline-formula> (susceptible node), modulating how past exposures shape future adoption likelihood. The influence strength evolves through state-dependent reinforcement and decay: sustained exposure from an actively disinformed node strengthens <inline-formula> <tex-math>$theta_{ij}(t)$</tex-math> </inline-formula>, while inactivity or countervailing signals induce relaxation. At the node level, belief accumulates through a leaky thresholded response to aggregated neighbor influence, so adoption emerges from cumulative exposure rather than isolated contact. This coupled mechanism enables influence strength, susceptibility, and belief to co-evolve over time. As a result, the model captures diffusion patterns that are difficult to reproduce within static transmission frameworks, including delayed activation, nonlinear reinforcement, echo-chamber amplification, and saturation shaped by structural bottlenecks. By systematically varying adaptive parameters, we demonstrate a spectrum of diffusion regimes ranging from slow persuasion to abrupt tipping and structurally constrained spread. Overall, the proposed model provides a mechanistic and history-informed representation of disinformation diffusion and offers a foundation for improved detection and intervention strategies.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6096-6116"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097268","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 Multivisual Cue-Based Approach for Intelligent Assessment of Student Engagement in the Classroom 基于多视觉线索的学生课堂参与度智能评估方法

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-07-14 DOI: 10.1109/TCSS.2026.3709309

Yaping Xu;Weitong Guo;Hongwu Yang

{"title":"A Multivisual Cue-Based Approach for Intelligent Assessment of Student Engagement in the Classroom","authors":"Yaping Xu;Weitong Guo;Hongwu Yang","doi":"10.1109/TCSS.2026.3709309","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3709309","url":null,"abstract":"Accurately assessing student engagement in classroom environments is essential for understanding learning processes and providing evidence to support instructional improvement. However, existing studies are often limited by restricted engagement dimensions, insufficient model generalizability, and a lack of process-oriented analysis. To address these challenges, this study proposes a multivisual cue-based framework for intelligent classroom engagement assessment by integrating object detection and tracking, facial expression recognition, and learning behavior recognition. To support model development and evaluation, a classroom-oriented academic emotion dataset containing 26 842 annotated facial-expression instances and a learning behavior dataset comprising 152 823 labeled behavior samples were constructed. Experimental results show that the facial expression recognition and learning behavior recognition models achieved accuracies of 84.1% and 91.4%, respectively. Based on the recognized emotional and behavioral states, emotional engagement, behavioral engagement, and overall engagement were dynamically assessed at both the individual and classroom levels. A case study conducted on authentic classroom videos further demonstrated the capability of the proposed framework to characterize the temporal evolution of student engagement and reveal engagement patterns across different instructional phases. These findings not only offer educators fine-grained insights into classroom dynamics but also establish a scalable foundation for real-time learning analytics, contributing to the advancement of intelligent classroom monitoring and pedagogical decision-making.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5760-5777"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097498","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

DiffCTRG: A Diffusion Model for City-Level Traffic Report Generation DiffCTRG：城市级交通报告生成的扩散模型

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-08-18 DOI: 10.1109/TCSS.2026.3718932

Chengyang Zhang;Yong Zhang;Qitan Shao;Bo Li;Yisheng Lv;Xinglin Piao;Baocai Yin

{"title":"DiffCTRG: A Diffusion Model for City-Level Traffic Report Generation","authors":"Chengyang Zhang;Yong Zhang;Qitan Shao;Bo Li;Yisheng Lv;Xinglin Piao;Baocai Yin","doi":"10.1109/TCSS.2026.3718932","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3718932","url":null,"abstract":"With the accumulation of massive traffic data in the era of intelligent transportation, automated city-level traffic report generation (CTRG) has emerged as an essential application. The CTRG aims to automatically generate textual summaries of road conditions from traffic data. It can serve as a new auxiliary decision-making tool for traffic management, which helps alleviate the workload of traffic managers and further improves the efficiency of traffic system management. However, similar to traditional image captioning tasks, CTRG faces the challenge of modeling the associations between different modalities of data. In this article, inspired by the impressive cross-modal data generation capability of diffusion models, we introduce the first baseline for the CTRG task, named diffusion-based CTRG (DiffCTRG). In DiffCTRG, we map textual descriptions to a latent space using a split bidirectional encoder representations from transformers (BERT), which aims to bridge the discrepancy between continuous diffusion processes and discrete textual data. In addition, a re-inference strategy is employed to enhance the correlation between different tokens according to the textual properties during the inference process. Extensive experiments on the Beijing text-traffic (BjTT) dataset demonstrate the effectiveness of DiffCTRG in generating high-quality traffic reports, establishing a baseline for this task.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5501-5513"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097550","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

MPA-MCDNet: Momentum ProtoNet for Cognitive Load Detection in Autonomous Vehicle Takeover via VR-Simulated Scenarios MPA-MCDNet：基于vr模拟场景的自动驾驶车辆接管认知负荷检测的动量原网

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-08-31 DOI: 10.1109/TCSS.2026.3720835

Ding Ding;Yufeng Jiang;Zhengxing Jiao

引用次数: 0

Nonrational Game-Theoretic Opinion Dynamics for Privacy-Preserving Distributed Consensus in Multiagent Systems 多智能体系统中保护隐私的分布式共识的非理性博弈论意见动力学

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-07-28 DOI: 10.1109/TCSS.2026.3712025

Yang Liu;Changping Yu;Zewei Zheng;Wenling Li;Jia Song

{"title":"Nonrational Game-Theoretic Opinion Dynamics for Privacy-Preserving Distributed Consensus in Multiagent Systems","authors":"Yang Liu;Changping Yu;Zewei Zheng;Wenling Li;Jia Song","doi":"10.1109/TCSS.2026.3712025","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3712025","url":null,"abstract":"This article addresses distributed consensus and collective intelligence in multiagent systems applied to social computing contexts, where agents must coordinate under privacy constraints and bounded rationality. We propose a computation framework integrating adaptive opinion dynamics, graph neural networks (GNNs), deep reinforcement learning (DRL), and federated learning (FL) within a nonrational game-theoretic foundation. The key contribution is an enhanced opinion dynamics model incorporating time-varying confidence bounds and asymmetric influence mechanisms, enabling exponential consensus beyond classical bounded confidence models. A privacy-preserving GNN-DRL-FL architecture achieves distributed coordination through spatial aggregation and decentralized parameter synchronization with <inline-formula><tex-math>$epsilon$</tex-math></inline-formula>-differential privacy guarantees. The coupling between opinion consensus and behavioral coordination is proven stable via linear matrix inequality (LMI) optimization. Theoretical analysis confirms convergence to Nash equilibrium, despite incomplete information and cognitive limitations. Comprehensive simulations covering comparative, scalability, privacy-ablation, and robustness studies confirm consistent gains in collective reward, energy efficiency, conflict avoidance, and privacy resilience over competing baselines.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5831-5847"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097712","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

Frequency Meets Intent: Enhancing Sequential Recommendation With Spectral Contrastive Learning 频率符合意图：用频谱对比学习增强顺序推荐

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-06-26 DOI: 10.1109/TCSS.2026.3704826

Jiahao Hu;Wei Zhou;Fengji Luo;Junlin Zhu;Junhao Wen;Hongyu Zhang

{"title":"Frequency Meets Intent: Enhancing Sequential Recommendation With Spectral Contrastive Learning","authors":"Jiahao Hu;Wei Zhou;Fengji Luo;Junlin Zhu;Junhao Wen;Hongyu Zhang","doi":"10.1109/TCSS.2026.3704826","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3704826","url":null,"abstract":"Sequential recommendation leverages interaction sequences to model users’ interests and provide personalized recommendations. However, most existing models focus on modeling users’ long-term preferences (low-frequency information) and struggle to perceive sudden changes in users’ interests, limiting the model’s ability to capture users’ short-term preferences (high-frequency information). Furthermore, they rely on the sequential relationship of users’ interaction sequences for modeling, failing to fully capture effective users’ preference information when interaction sequences are sparse, consequently affecting the accurate modeling of users’ latent intents. In this article, we propose an intent-aware spectrally enhanced contrastive learning sequential recommendation model (ISECLRec). Specifically, we design a frequency adaptive reconstruction module to adjust the frequency-domain representation of the sequence to enhance the model’s ability to capture users’ short-term preferences. Meanwhile, we introduce a calibration factor to linearly combine the reconstructed frequency-domain representation with the original sequence representation, comprehensively modeling users’ long-term and short-term preferences. In addition, we construct shared latent intent prototypes and align users’ interaction sequences with their target intents, enabling ISECLRec to accurately capture users’ latent intents. Extensive experimental results on six real-world datasets demonstrate that ISECLRec outperforms advanced baseline models by an average of 4.34% and 3.71% in HR and NDCG, respectively.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5647-5661"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097665","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