IEEE Transactions on Computational Social Systems最新文献_第3页

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

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.2026.3730934

引用次数: 0

Supplier Qualification of the Production Chain in Collaborative Manufacturing via Higher Order Group Multi-Role Assignment 基于高阶组多角色分配的协同制造生产链供应商资格

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.2026.3710531

Junhao Chen;Haibin Zhu;Yong Zhang;Yinghui Pan;Dongning Liu

{"title":"Supplier Qualification of the Production Chain in Collaborative Manufacturing via Higher Order Group Multi-Role Assignment","authors":"Junhao Chen;Haibin Zhu;Yong Zhang;Yinghui Pan;Dongning Liu","doi":"10.1109/TCSS.2026.3710531","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3710531","url":null,"abstract":"In collaborative manufacturing, the customer-to-manufacturer model establishes a direct link between users and suppliers, facilitating a production process driven by personalized demands. Users publish multiple product demands, generating corresponding production tasks, while suppliers are responsible for completing these tasks. This creates higher order many-to-many relationships among users, products, and suppliers, making task assignment more challenging. For this concern, this article formalizes the production task assignment problem in collaborative manufacturing (PTAPCM) by revising the group multi-role assignment (GMRA) model to a higher order GMRA (HOGMRA) model. In HOGMRA, distinguishing the capabilities of each supplier becomes the key issue in solving the PTAPCM. To address this, we propose the supplier qualification algorithm (SQA) and its enhanced version, enhanced supplier qualification algorithm (ESQA). After evaluating supplier qualifications, HOGMRA formulates the PTAPCM as an integer linear programming problem that can be solved by a general-purpose optimizer. Finally, large-scale experiments are carried out to demonstrate the practicability and effectiveness of the proposed solutions.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5848-5861"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149096977","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

DQ4FairIM: Fairness-Aware Influence Maximization Using Deep Reinforcement Learning 使用深度强化学习实现公平感知影响最大化

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-07-01 DOI: 10.1109/TCSS.2026.3705520

Akrati Saxena;Harshith Kumar Yadav;Bart Rutten;Shashi Shekhar Jha

{"title":"DQ4FairIM: Fairness-Aware Influence Maximization Using Deep Reinforcement Learning","authors":"Akrati Saxena;Harshith Kumar Yadav;Bart Rutten;Shashi Shekhar Jha","doi":"10.1109/TCSS.2026.3705520","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3705520","url":null,"abstract":"The influence maximization (IM) problem aims to select a set of seed nodes within a given budget to maximize the spread of influence in a social network. However, real-world social networks have several structural inequalities, such as dominant majority groups and underrepresented minority groups. If these inequalities are not considered while designing IM algorithms, the outcomes might be biased, disproportionately benefiting majority groups while marginalizing minorities. In this work, we address this gap by designing a fairness-aware IM method using deep reinforcement learning (RL) that ensures equitable influence outreach across all communities, regardless of protected attributes. Fairness is incorporated using the maximin fairness objective, which prioritizes improving the outreach of the least-influenced group, pushing the solution toward an equitable influence distribution. We propose a novel fairness-aware deep RL method, called deep <inline-formula><tex-math>$boldsymbol{Q}$</tex-math></inline-formula>-learning for fair influence maximization (DQ4FairIM), that learns a seeding policy to maximize the expected number of influenced nodes, ensuring that minority groups are not disproportionately excluded. The IM problem is formulated as a Markov decision process and solved using deep Q-learning combined with Structure2vec network embeddings. The main benefit of using RL for this problem is its generalization ability, in which a policy learnt on a specific problem instance can be generalized to new problem instances without retraining the model from scratch. Extensive experiments on synthetic benchmarks and real-world networks demonstrate that DQ4FairIM consistently achieves higher fairness and a better fairness–performance trade-off compared to both fairness-agnostic and existing fairness-aware baselines, while retaining strong generalization across varying network sizes and seed budgets.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5581-5594"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097040","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

BiDepNet: A Bidirectional Enhancement Network for Depression Detection via Facial Expression Analysis BiDepNet：一种基于面部表情分析的抑郁症检测双向增强网络

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-04-17 DOI: 10.1109/TCSS.2026.3670016

Min Hu;Yongkang Lei;Xiaohua Wang;Lei He;Jiaoyun Yang;Ning An

{"title":"BiDepNet: A Bidirectional Enhancement Network for Depression Detection via Facial Expression Analysis","authors":"Min Hu;Yongkang Lei;Xiaohua Wang;Lei He;Jiaoyun Yang;Ning An","doi":"10.1109/TCSS.2026.3670016","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3670016","url":null,"abstract":"Research has shown that facial expressions can effectively infer the severity of depression. Recently, depression assessment methods based on images or facial landmarks have gained widespread attention. However, existing methods have not fully considered the complementary and synergistic effects between facial landmark features and images in extracting depression cues. Therefore, this article proposes a bidirectional depression detection network, aimed at comprehensively capturing facial depression cues. We employ a differential feature enhancement module to extract the differences between features and inject them into the corresponding features. Simultaneously, the common feature enhancement module utilizes facial landmark-based structured positional information as a guiding signal, directing the model to focus on depression-related information within the facial image features and fuse shared features. Furthermore, through adaptive feature fusion, we enhance the expressive power of depression-related features to improve the accuracy of assessment. We conducted extensive experiments on the AVEC2014 depression dataset and the RAF-DB facial expression dataset. The experimental results demonstrate that our method outperforms the current state-of-the-art methods, providing strong evidence of its effectiveness.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6786-6797"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097074","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

Modelling the Closed-Loop Dynamics Between a Social Media Recommender System and Users’ Opinions 社交媒体推荐系统与用户意见的闭环动力学建模

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-07-31 DOI: 10.1109/TCSS.2026.3715162

Ella C. Davidson;Mengbin Ye

{"title":"Modelling the Closed-Loop Dynamics Between a Social Media Recommender System and Users’ Opinions","authors":"Ella C. Davidson;Mengbin Ye","doi":"10.1109/TCSS.2026.3715162","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3715162","url":null,"abstract":"This article proposes a mathematical model to study the coupled dynamics of a recommender system (RS) algorithm and content consumers (users). The model posits that a large population of users, each with an opinion, consumes personalised content recommended by the RS. The RS can select from a range of content to recommend based on users’ past engagement, while users can engage with the content (e.g., like or watch), and in doing so, their opinions evolve. This occurs repeatedly to capture the endless content available for user consumption on social media. We employ a campaign of Monte Carlo simulations to study how RSs influence users’ opinions and, in turn, how users’ opinions shape subsequent recommended content. Both the performance of the RS (e.g., how users engage with the content) and the polarisation and radicalisation of users’ opinions are of interest. We find that different opinion distributions are more susceptible to polarisation than others, that many content stances are ineffective at changing user opinions, and that creating viral content is an effective measure for combating opinion polarisation.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"5778-5790"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097151","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 Comprehensive Review of Microexpression Recognition, Classification, and Datasets 微表情识别、分类和数据集综述

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-03-06 DOI: 10.1109/TCSS.2026.3672552

Parul Malik;Jaiteg Singh;Farman Ali;Daehan Kwak

{"title":"A Comprehensive Review of Microexpression Recognition, Classification, and Datasets","authors":"Parul Malik;Jaiteg Singh;Farman Ali;Daehan Kwak","doi":"10.1109/TCSS.2026.3672552","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3672552","url":null,"abstract":"Facial microexpressions (MEs) are brief, involuntary facial movements that reveal genuine emotions a person attempts to suppress or conceal. Their short duration and low intensity pose considerable challenges for human observers and automated recognition systems, yet MEs hold practical value in psychotherapy, deception detection, marketing, and public safety. Several surveys have reviewed ME recognition, but most are primarily descriptive and lack systematic frameworks for literature selection and analysis. They also tend to overlook how individual, expression-related, and contextual factors shape recognition outcomes. This study addresses these limitations through a preferred reporting items for systematic reviews and meta-analyses (PRISMA)-based systematic review of ME recognition research published between 2015 and 2025, covering preprocessing methods, feature representation, and dataset characteristics. The review critically compares existing approaches, weighing their strengths, limitations, and suitability for real-world deployment. It examines persistent challenges, including dataset imbalance, limited cross-dataset generalization, and underexplored temporal dynamics, alongside emerging directions in action unit-based, multimodal, and deep learning methods. Specific recommendations for future work aimed at closing the gap between laboratory findings and practical application are also provided. By grounding the analysis in transparent selection criteria and structured synthesis, this work offers a reproducible foundation for advancing ME recognition research.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6212-6227"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097305","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

MB-HGTBot: Modeling Multitype Social Behaviors in Heterogeneous Graphs for Robust Twitter Bot Detection MB-HGTBot：在异构图中建模多类型社会行为，用于稳健的Twitter Bot检测

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-03-04 DOI: 10.1109/TCSS.2026.3682474

Qiang Liu;Weisha Zhang;Jiazhong Lu

{"title":"MB-HGTBot: Modeling Multitype Social Behaviors in Heterogeneous Graphs for Robust Twitter Bot Detection","authors":"Qiang Liu;Weisha Zhang;Jiazhong Lu","doi":"10.1109/TCSS.2026.3682474","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3682474","url":null,"abstract":"Existing studies predominantly utilize the topology of the Twittersphere to counteract the challenge of social bots circumventing detection via feature manipulation. However, the heterogeneity of users’ social behavioral patterns remains largely underexplored. To address this issue, we propose <bold>MB-HGTBot</b>, a social behavior scenario-oriented framework for Twitter bot detection. Our approach effectively extracts and integrates multimodal user semantic representations with numerical attributes while optimizing feature learning to improve the accuracy of bot detection. Specifically, we construct heterogeneous information networks (HINs) based on diverse social behaviors, enabling a more comprehensive characterization of structural variations underlying diverse social interactions, thereby significantly improving detection performance. Experimental results show that MB-HGTBot outperforms state-of-the-art methods, achieving an accuracy of <bold>88.29%</b> across six distinct social behavior patterns in the Twibot-22 dataset and three real-world topic-specific contexts. Furthermore, our findings highlight the pivotal role of social behavior diversity in Twitter bot detection and provide novel insights into addressing the challenges posed by manipulated features. The implementation of MB-HGTBot is available at <uri>https://github.com/Bemcliu/MB_HGTBot</uri>.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6174-6195"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097299","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

Multilabel Emotion Recognition in Urdu Using Attention-Based Hybrid Model and Fractional Order Backpropagation 基于注意混合模型和分数阶反向传播的乌尔都语多标签情感识别

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-02-03 DOI: 10.1109/TCSS.2025.3647092

Kirti Kumari;Shirish Shekhar Jha

{"title":"Multilabel Emotion Recognition in Urdu Using Attention-Based Hybrid Model and Fractional Order Backpropagation","authors":"Kirti Kumari;Shirish Shekhar Jha","doi":"10.1109/TCSS.2025.3647092","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3647092","url":null,"abstract":"Emotion detection in text has been widely researched for high-resource languages, yet low-resource languages such as Urdu remain underexplored. The present work addresses this gap by developing a multilabel emotion detection framework for Urdu using a fractional-order backpropagation learning approach. The work employs two complementary sentence embedding techniques—one supervised (paraphrase-multilingual) and one unsupervised (transformer-based sequential denoising autoencoder)—in combination with long short-term memory (LSTM) and logistic regression classifiers. Experiments are conducted on the publicly available Urdu Emothreat dataset released as a part of 2022 Forum for Information Retrieval (FIRE), containing 7800 labeled sentences across seven emotion categories. The proposed fractional backpropagation method achieves a consistent 4% average improvement in F1-score over conventional integer-order backpropagation, highlighting its potential for more stable and generalized learning in low-resource settings. The best-performing model, based on supervised LSTM embeddings, attains a weighted precision of 66%, recall of 85%, and F1-score of 74%. These findings establish a strong baseline for Urdu emotion recognition and demonstrate the practical significance of fractional-order optimization for multilingual and low-resource NLP tasks.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6459-6468"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097325","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

Nash Equilibrium Seeking for Hybrid Heterogeneous Euler–Lagrange Systems in Multicluster Games Under DoS Attacks: A Predefined-Time Approach DoS攻击下混合异构Euler-Lagrange系统的纳什均衡寻求：一种预定义时间方法

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-01-21 DOI: 10.1109/TCSS.2025.3649613

Shuting Chen;Ying Wan;Leszek Rutkowski;Liang Hua;Jinde Cao

{"title":"Nash Equilibrium Seeking for Hybrid Heterogeneous Euler–Lagrange Systems in Multicluster Games Under DoS Attacks: A Predefined-Time Approach","authors":"Shuting Chen;Ying Wan;Leszek Rutkowski;Liang Hua;Jinde Cao","doi":"10.1109/TCSS.2025.3649613","DOIUrl":"https://doi.org/10.1109/TCSS.2025.3649613","url":null,"abstract":"This article investigates the problem of generalized Nash equilibrium (NE) seeking in multicluster games involving hybrid heterogeneous Euler–Lagrange (EL) systems under denial-of-service (DoS) attack. Each cluster aims to minimize its payoff function, integrating noncooperative games with distributed optimization in a unified framework. Traditional generalized Nash equilibrium (GNE) seeking methods often assume linear dynamics, which are insufficient for real-world applications involving nonlinear hybrid heterogeneous EL systems. Initially, the problem is addressed without considering DoS attacks, where a distributed predefined-time control method is developed to achieve GNE among players in the multicluster game. This approach ensures convergence to the NE within a predefined-time frame. Moreover, in complex DoS environments, achieving GNE becomes challenging when the communication links between agents are attacked. To address this, we propose a topology reconstruction method by restoring and reconnecting the communication connection based on their positions. Meanwhile, the sufficient conditions to GNE seeking in predefined-time under DoS attacks are presented. Numerical simulations are provided to validate the effectiveness of our method in multicluster game scenarios, demonstrating its practical applicability in both DoS attacks and non-attack situations.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6304-6315"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097429","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

ADAPT-MIND: A Cross-Domain Deep Learning Framework for Mental Health Signal Detection in Shakespearean and Social Media Texts ADAPT-MIND：莎士比亚和社交媒体文本中心理健康信号检测的跨领域深度学习框架

IF 4.6 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2026-10-01 Epub Date: 2026-02-06 DOI: 10.1109/TCSS.2026.3654355

Avinash Samantra;Durga Prasad Mohapatra

{"title":"ADAPT-MIND: A Cross-Domain Deep Learning Framework for Mental Health Signal Detection in Shakespearean and Social Media Texts","authors":"Avinash Samantra;Durga Prasad Mohapatra","doi":"10.1109/TCSS.2026.3654355","DOIUrl":"https://doi.org/10.1109/TCSS.2026.3654355","url":null,"abstract":"Cross-domain detection of mental distress signals in historical and contemporary texts is very challenging because of linguistic heterogeneity and the limited annotation resources. Adapter-Augmented Distillation And Prompt-Tuning – Memory-Augmented INference for Distress (ADAPT-MIND) proposes a unified framework that integrates adapter-enhanced LLM embeddings, graph-attentive temporal convolutions, memory-augmented transformers, adversarial–contrastive distillation and mutual information regularization, and mixed meta-learned soft prompts. Shared pre-processing and feature augmentation are applied to both Shakespearean utterances (10% expert-annotated, <inline-formula><tex-math>$kappa = 0.82$</tex-math></inline-formula>) and 7489 student social-media posts (80/10/10 split), including pronoun variance, affective bigrams, syntactic entropy, and graph positional encodings. On five-fold cross-validation, ADAPT-MIND achieves accuracies of 0.912 for Shakespearean and 0.857 for students, with F1 scores of 0.911 and 0.856 and AUC of 0.948 and 0.903, respectively. Extensive ablation studies confirm an F1 drop of up to 4.2% by removing the graph-temporal module, proving its critical role in capturing multiscale narrative context. Error analysis shows misunderstanding of archaic idioms is responsible for 31%, while the rest is mainly due to mistaken academic stress, totaling 27%. Hyper-parameter optimization utilizes hierarchical Bayesian search over loss weights, learning rates, adapter dimensions, and convolution depth. The proposed approach delivers state-of-the-art performance across domains while ensuring interpretability and efficient parameter adaptation.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"13 5","pages":"6731-6746"},"PeriodicalIF":4.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149097531","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