IEEE Transactions on Computational Social Systems最新文献_第2页

Socially Enhanced Defense in Energy-Transportation Systems 能源运输系统的社会增强防御

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2025-01-09 DOI: 10.1109/TCSS.2024.3517140

Alexis Pengfei Zhao;Shuangqi Li;Yunqi Wang;Mohannad Alhazmi

{"title":"Socially Enhanced Defense in Energy-Transportation Systems","authors":"Alexis Pengfei Zhao;Shuangqi Li;Yunqi Wang;Mohannad Alhazmi","doi":"10.1109/TCSS.2024.3517140","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3517140","url":null,"abstract":"The ever-increasing entwinement of information and communication technology (ICT) infrastructure with the proliferation of electric vehicles (EVs) has resulted in a congruent coalescence of energy and transportation networks. However, the surfeit of data communication and processing capabilities inherent in these systems also poses a potential peril to cyber security. Hence, a bifurcated logistics operation and cyberattack defense strategy have been propounded for green integrated power-transportation networks (IPTN) with renewable penetration. This strategy leverages the potential of social participation from EVs to amplify the defense operation. The bifurcation comprises of a preclusive stage aimed at fortifying and preserving resource allocation within IPTN and a defensive stage aimed at mitigating the deleterious impacts of cyberattacks through rapid response measures. Conventional measures such as load shedding and operation adjustments are augmented by an innovative defense involvement incentive, designed to elicit additional support from EV users. A mean-risk distributionally robust optimization methodology predicated on Kullback–Leibler divergence is posited to address the limitations in data availability in simulating cyberattack consequences. Empirical investigations through case studies in an urbane IPTN are conducted to evaluate the adverse impacts of cyberattacks and examine countermeasures aimed at mitigating their effects to the greatest extent possible.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"563-572"},"PeriodicalIF":4.5,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783264","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

Unraveling the Deception of Web3 Phishing Scams: Dynamic Multiperspective Cascade Graph Approach for Ethereum Phishing Detection 揭露Web3网络钓鱼诈骗的欺骗：动态多角度级联图方法用于以太坊网络钓鱼检测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-31 DOI: 10.1109/TCSS.2024.3516144

Lejun Zhang;Xucan Zhang;Siyi Xiao;Zexin Li;Shen Su;Jing Qiu;Zhihong Tian

{"title":"Unraveling the Deception of Web3 Phishing Scams: Dynamic Multiperspective Cascade Graph Approach for Ethereum Phishing Detection","authors":"Lejun Zhang;Xucan Zhang;Siyi Xiao;Zexin Li;Shen Su;Jing Qiu;Zhihong Tian","doi":"10.1109/TCSS.2024.3516144","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3516144","url":null,"abstract":"Ethereum, as one of the most active cryptocurrency trading platforms, has garnered significant academic interest due to its transparent and accessible transaction data. In recent years, phishing scams have emerged as a serious criminal activity on Ethereum. Although most studies model Ethereum account transactions as networks and analyze them using traditional machine learning or network representation learning techniques, these approaches often rely solely on the latest static transaction records or use manually designed features while neglecting transaction histories, thus failing to fully capture the dynamic interactions and potential trading patterns between accounts. This article introduces an innovative multiperspective cascaded dynamic graph neural network model named DMPCG, which extracts phishing transaction data from authoritative databases like blockchain explorers to construct transaction network graphs. The model elevates the analysis from the microscopic features of nodes to the macroscopic dynamics of the entire network, integrating the attributes of static snapshot graphs with the evolution of dynamic trading networks, significantly enhancing the accuracy of phishing detection. Experimental results demonstrate that the DMPCG method achieves an impressive precision of 92.6% and an F1-score of 90.9%, outperforming existing baseline models and traditional subgraph sampling techniques.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"498-510"},"PeriodicalIF":4.5,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783261","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

Multibranch Attentive Transformer With Joint Temporal and Social Correlations for Traffic Agents Trajectory Prediction 具有联合时间和社会关联的多支路关注变压器交通agent轨迹预测

IF 4.5 2区计算机科学

IEEE Transactions on Computational Social Systems Pub Date : 2024-12-24 DOI: 10.1109/TCSS.2024.3517656

Xiaobo Chen;Yuwen Liang;Junyu Wang;Qiaolin Ye;Yingfeng Cai

{"title":"Multibranch Attentive Transformer With Joint Temporal and Social Correlations for Traffic Agents Trajectory Prediction","authors":"Xiaobo Chen;Yuwen Liang;Junyu Wang;Qiaolin Ye;Yingfeng Cai","doi":"10.1109/TCSS.2024.3517656","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3517656","url":null,"abstract":"Accurately predicting the future trajectories of traffic agents is paramount for autonomous unmanned systems, such as self-driving cars and mobile robotics. Extracting abundant temporal and social features from trajectory data and integrating the resulting features effectively pose great challenges for predictive models. To address these issues, this article proposes a novel multibranch attentive transformer (MBAT) trajectory prediction network for traffic agents. Specifically, to explore and reveal diverse correlations of agents, we propose a decoupled temporal and spatial feature learning module with multibranch to extract temporal, spatial, as well as spatiotemporal features. Such design ensures each branch can be specifically tailored for different types of correlations, thus enhancing the flexibility and representation ability of features. Besides, we put forward an attentive transformer architecture that simultaneously models the complex correlations possibly occurring in historical and future timesteps. Moreover, the temporal, spatial, and spatiotemporal features can be effectively integrated based on different types of attention mechanisms. Empirical results demonstrate that our model achieves outstanding performance on public ETH, UCY, SDD, and INTERACTION datasets. Detailed ablation studies are conducted to verify the effectiveness of the model components.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"525-538"},"PeriodicalIF":4.5,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783268","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

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

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

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