IEEE Transactions on Computational Social Systems最新文献_第4页

Unveiling Agents’ Confidence in Opinion Dynamics Models via Graph Neural Networks 基于图神经网络的意见动态模型中主体信心的揭示

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-11 DOI: 10.1109/TCSS.2024.3508452

Víctor A. Vargas-Pérez;Jesús Giráldez-Cru;Pablo Mesejo;Oscar Cordón

{"title":"Unveiling Agents’ Confidence in Opinion Dynamics Models via Graph Neural Networks","authors":"Víctor A. Vargas-Pérez;Jesús Giráldez-Cru;Pablo Mesejo;Oscar Cordón","doi":"10.1109/TCSS.2024.3508452","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3508452","url":null,"abstract":"Opinion Dynamics models in social networks are a valuable tool to study how opinions evolve within a population. However, these models often rely on agent-level parameters that are difficult to measure in a real population. This is the case of the confidence threshold in opinion dynamics models based on bounded confidence, where agents are only influenced by other agents having a similar opinion (given by this confidence threshold). Consequently, a common practice is to apply a universal threshold to the entire population and calibrate its value to match observed real-world data, despite being an unrealistic assumption. In this work, we propose an alternative approach using graph neural networks to infer agent-level confidence thresholds in the opinion dynamics of the Hegselmann-Krause model of bounded confidence. This eliminates the need for additional simulations when faced with new case studies. To this end, we construct a comprehensive synthetic training dataset that includes different network topologies and configurations of thresholds and opinions. Through multiple training runs utilizing different architectures, we identify GraphSAGE as the most effective solution, achieving a coefficient of determination <inline-formula><tex-math>$R^{2}$</tex-math></inline-formula> above 0.7 in test datasets derived from real-world topologies. Remarkably, this performance holds even when the test topologies differ in size from those considered during training.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"725-737"},"PeriodicalIF":4.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10792931","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Community-Enhanced Dynamic Graph Convolutional Networks for Rumor Detection on Social Networks 基于社区增强的动态图卷积网络的社交网络谣言检测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-11 DOI: 10.1109/TCSS.2024.3505892

Wei Zhou;Chenzhan Wang;Fengji Luo;Yu Wang;Min Gao;Junhao Wen

{"title":"Community-Enhanced Dynamic Graph Convolutional Networks for Rumor Detection on Social Networks","authors":"Wei Zhou;Chenzhan Wang;Fengji Luo;Yu Wang;Min Gao;Junhao Wen","doi":"10.1109/TCSS.2024.3505892","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3505892","url":null,"abstract":"Along with the increasing popularization of social platforms, rumors in the Web environment have become one of the significant threats to human society. Existing rumor detection methods ignore modeling and analyzing the community structure of the rumor propagation network. This article proposes a new community-enhanced dynamic graph convolutional network (CDGCN) for effective rumor detection on online social networks, which utilize the communities formed in a rumor propagation process to improve rumor detection accuracy. CDGCN uses a designed method that combines node features and topology features to identify the communities and learn the community features of rumors. Following this, a graph convolutional network (GCN) with a community-aware attention mechanism is proposed to enable the nodes to dynamically aggregate information from their neighboring nodes’ global and community features, effectively prioritizing critical neighborhood information, enhancing the representation of both local community structures and global network patterns for improved analytical performance. The final rumor representations generated by the GCN are processed by a classifier to detect false rumors. Comprehensive experiments and comparison studies are conducted on four real-world datasets to validate the effectiveness of CDGCN.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"818-831"},"PeriodicalIF":4.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769473","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

Maximizing Group Utilities While Avoiding Conflicts Through Agent Qualifications 通过代理资格最大化群体效用同时避免冲突

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-11 DOI: 10.1109/TCSS.2024.3504398

Keyi Chen;Tianxing Wang;Haibin Zhu;Bing Huang

引用次数: 0

Preserving Social Relationship Privacy via the Exponential Mechanism of Personalized Differential Privacy 基于个性化差异隐私指数机制的社会关系隐私保护

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-11 DOI: 10.1109/TCSS.2024.3508744

Jiawei Shen;Junfeng Tian;Ziyuan Wang;Qi Zhu

{"title":"Preserving Social Relationship Privacy via the Exponential Mechanism of Personalized Differential Privacy","authors":"Jiawei Shen;Junfeng Tian;Ziyuan Wang;Qi Zhu","doi":"10.1109/TCSS.2024.3508744","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3508744","url":null,"abstract":"Presently, the majority of social networking platforms tend to outsource the analysis of social relationship data to third-party companies. Existing methods, which generally aim to protect social relationships by erasing friendship links or introducing uniform noise into datasets, do not take into account the risk of inference attacks or the actual privacy needs of users. To address these concerns, we present a novel method, named exponential mechanism of personalized difference privacy (EPDP), for preserving the privacy of social relationships, based on the EPDP. We develop specific social relationship indices to group friendship links and divided these links into distinct privacy levels, each with a unique privacy budget. Then, we select representative elements from each group using sampling and the exponential mechanism to generalize the original datasets, ensuring compliance with personalized difference privacy principles. Metrics for privacy and utility assessment are devised to evaluate method performance. Experimental results reveal that EPDP offers superior utility compared to uniform differential privacy (UDP) and provides better privacy protection than the state-of-the-art. Moreover, we explore the impact of various parameters on data utility. This article marks the pioneering effort to introduce a privacy-preserving method based on the exponential mechanism for the safeguarding of social relationships.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1164-1180"},"PeriodicalIF":4.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178885","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

DataPoll: A Tool Facilitating Big Data Research in Social Sciences DataPoll：促进社会科学大数据研究的工具

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-09 DOI: 10.1109/TCSS.2024.3506582

Antonis Charalampous;Constantinos Djouvas;Christos Christodoulou

{"title":"DataPoll: A Tool Facilitating Big Data Research in Social Sciences","authors":"Antonis Charalampous;Constantinos Djouvas;Christos Christodoulou","doi":"10.1109/TCSS.2024.3506582","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3506582","url":null,"abstract":"The computational analysis of big data has revolutionized social science research, offering unprecedented insights into societal behaviors and trends through digital data from online sources. However, existing tools often face limitations such as technical complexity, single-source dependency, and a narrow range of analytical capabilities, hindering accessibility and effectiveness. This article introduces DataPoll, an end-to-end big data analysis platform designed to democratize computational social science research. DataPoll simplifies data collection, analysis, and visualization, making advanced analytics accessible to researchers of diverse expertise. It supports multisource data integration, innovative analytical features, and interactive dashboards for exploratory and comparative analyses. By fostering collaboration and enabling the integration of new data sources and analysis methods, DataPoll represents a significant advancement in the field. A comprehensive case study on the Ukrainian–Russian conflict demonstrates its capabilities, showcasing how DataPoll can yield actionable insights into complex social phenomena. This tool empowers researchers to harness the potential of big data for impactful and inclusive research.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"511-524"},"PeriodicalIF":4.5,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783365","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

2024 Index IEEE Transactions on Computational Social Systems Vol. 11 计算社会系统学报第11卷

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-05 DOI: 10.1109/TCSS.2024.3512113

引用次数: 0

RF-KDE-QSR Model for Estimating the Scale of Epidemics 流行病规模估计的RF-KDE-QSR模型

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-05 DOI: 10.1109/TCSS.2024.3507733

Chuwei Liu;Jianping Huang;Siyu Chen;Jiaqi He;Shikang Du;Nan Yin;Chao Zhang;Danfeng Wang

{"title":"RF-KDE-QSR Model for Estimating the Scale of Epidemics","authors":"Chuwei Liu;Jianping Huang;Siyu Chen;Jiaqi He;Shikang Du;Nan Yin;Chao Zhang;Danfeng Wang","doi":"10.1109/TCSS.2024.3507733","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3507733","url":null,"abstract":"Infectious diseases are posing an increasingly serious threat to human society. It is urgent to make a rapid estimate of the scale of outbreaks when the disease information is still unclear in the early stages of the outbreak, so as to buy time for a timely response to infectious diseases and provide reference for the allocation of medical resources and the formulation of control measures. Based on this, this study took the concentrated outbreak of COVID-19 in various cities in China as an example, collected 22 meteorological, social-ecological and population mobility indicators, and established a random forest-kernel density estimation-quantile stepwise regression (RF-KDE-QSR) model to make a preliminary estimate of the daily outbreak scale in cities. The RF model was used for preliminary estimation, and the KDE-QSR model was used for residual correction to correct the prediction results. The evaluation of the prediction accuracy proved the effectiveness of the prediction model. When the RF model was used alone, the R-squared (R<sup>2</sup>) was 0.82 and the corrected R<sup>2</sup> was 0.90. The KDE-QSR model effectively improved the prediction accuracy of the model.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1193-1201"},"PeriodicalIF":4.5,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10779452","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","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 : 2024-12-02 DOI: 10.1109/TCSS.2024.3493357

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 : 2024-12-02 DOI: 10.1109/TCSS.2024.3493359

引用次数: 0

Metacracy: A New Governance Paradigm Beyond Bounded Intelligence 元统治：超越有限智能的新治理范式

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-02 DOI: 10.1109/TCSS.2024.3493372

Fei-Yue Wang;Rui Qin;Juanjuan Li;Levente Kováacs;Bin Hu

引用次数: 0