Pub Date : 2024-06-13DOI: 10.1109/TCSS.2024.3397403
Minqiang Yang;Yongfeng Tao;Hanshu Cai;Bin Hu
{"title":"Behavioral Information Feedback With Large Language Models for Mental Disorders: Perspectives and Insights","authors":"Minqiang Yang;Yongfeng Tao;Hanshu Cai;Bin Hu","doi":"10.1109/TCSS.2024.3397403","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3397403","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 3","pages":"3026-3044"},"PeriodicalIF":5.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557232","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319710","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}
Pub Date : 2024-06-13DOI: 10.1109/TCSS.2024.3397413
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/TCSS.2024.3397413","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3397413","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 3","pages":"C3-C3"},"PeriodicalIF":5.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557225","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319668","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}
Pub Date : 2024-06-13DOI: 10.1109/TCSS.2024.3397406
Tu Nguyen;Vincenzo Piuri;Joel Rodrigues;Lianyong Qi;Shahid Mumtaz;Warren Huang-Chen Lee
{"title":"Guest Editorial: Special Issue on Knowledge-Infused Learning for Computational Social Systems","authors":"Tu Nguyen;Vincenzo Piuri;Joel Rodrigues;Lianyong Qi;Shahid Mumtaz;Warren Huang-Chen Lee","doi":"10.1109/TCSS.2024.3397406","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3397406","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 3","pages":"3045-3048"},"PeriodicalIF":5.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557224","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319709","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}
Pub Date : 2024-06-13DOI: 10.1109/TCSS.2024.3397415
{"title":"IEEE Transactions on Computational Social Systems Information for Authors","authors":"","doi":"10.1109/TCSS.2024.3397415","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3397415","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 3","pages":"C4-C4"},"PeriodicalIF":5.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557243","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319647","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}
Pub Date : 2024-06-13DOI: 10.1109/TCSS.2024.3397411
{"title":"IEEE Transactions on Computational Social Systems Publication Information","authors":"","doi":"10.1109/TCSS.2024.3397411","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3397411","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 3","pages":"C2-C2"},"PeriodicalIF":5.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10557247","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141319642","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}
Geopolitical conflicts significantly impact financial networks and systems, e.g., Russia and Ukraine. Cryptoeconomic blockchains such as Bitcoin and Ethereum were introduced as substitutes for traditional financial systems and might behave differently under significant stress. The Russia–Ukraine conflict allowed us to analyze the impact of such complex geopolitical conflicts on the user behaviors of cryptoeconomic blockchains. This article investigates the early stage of such geopolitical conflict using time-varying graphs. We collected and analyzed all the transactions for Bitcoin and Ethereum that took place 2 weeks before and after the conflict started, i.e., we focused on what can be defined as the acute impact of such an event. Our results suggest that the early stage of such geopolitical conflicts may significantly affect cryptoeconomic blockchains’ user behaviors. For instance, we detected that some users behaved more cautiously during the preconflict phase and resumed normalcy during the postconflict phase but exhibited a shift in their behavior. This article analyzes the relationship between the early stages of geopolitical conflicts and cryptoeconomic systems.
{"title":"Cryptoeconomic User Behavior in the Acute Stages of Geopolitical Conflict","authors":"Jorão Gomes;Heder Bernardino;Alex Borges Vieira;Verena Dorner;Davor Svetinovic","doi":"10.1109/TCSS.2024.3404590","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3404590","url":null,"abstract":"Geopolitical conflicts significantly impact financial networks and systems, e.g., Russia and Ukraine. Cryptoeconomic blockchains such as Bitcoin and Ethereum were introduced as substitutes for traditional financial systems and might behave differently under significant stress. The Russia–Ukraine conflict allowed us to analyze the impact of such complex geopolitical conflicts on the user behaviors of cryptoeconomic blockchains. This article investigates the early stage of such geopolitical conflict using time-varying graphs. We collected and analyzed all the transactions for Bitcoin and Ethereum that took place 2 weeks before and after the conflict started, i.e., we focused on what can be defined as the acute impact of such an event. Our results suggest that the early stage of such geopolitical conflicts may significantly affect cryptoeconomic blockchains’ user behaviors. For instance, we detected that some users behaved more cautiously during the preconflict phase and resumed normalcy during the postconflict phase but exhibited a shift in their behavior. This article analyzes the relationship between the early stages of geopolitical conflicts and cryptoeconomic systems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"7055-7068"},"PeriodicalIF":4.5,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10556724","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368440","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}
Pub Date : 2024-06-12DOI: 10.1109/TCSS.2024.3394439
Wei Guan;Qing Guan;Yueran Duan;Changhong Xiang
Finding potential successful startups is always a key issue for industrial innovation and economic development, yet it poses a significant challenge due to the complexity of investments and low success rates. Compared with existing models on knowledge correlations among pairwise startups in a first-order perspective, potential dependencies among sequential investment behaviors reveal knowledge correlations among multiple startups, which requires modeling from a higher order perspective. In this article, a novel higher order network (HON) framework, generated by dependencies among investment behaviors with timestamps, is proposed to identify the pattern of knowledge flows among startups, which has been approved higher accuracy in predicting investment behaviors. Moreover, we introduce a HON-based centrality indicator to measure the importance of startups. Experiments compared with baseline models have shown that the startups identified by proposed indicator are more influential in knowledge propagation and are closer to success. An empirical study conducted by Crunchbase database further reveals that internet-based startups occupy a significant position in investment landscapes, with those associated with finance and commerce not only attracting considerable investments but also facilitating greater success for related startups.
寻找潜在的成功初创企业一直是产业创新和经济发展的关键问题，但由于投资的复杂性和较低的成功率，这构成了巨大的挑战。与现有的以一阶视角研究成对初创企业之间知识相关性的模型相比，连续投资行为之间的潜在依赖关系揭示了多个初创企业之间的知识相关性，这就需要从高阶视角进行建模。本文提出了一个新颖的高阶网络（HON）框架，该框架由带有时间戳的投资行为之间的依赖关系生成，用于识别初创企业之间的知识流动模式，在预测投资行为方面具有更高的准确性。此外，我们还引入了基于 HON 的中心性指标来衡量初创企业的重要性。与基线模型相比，实验表明，用提出的指标识别出的初创企业在知识传播方面更有影响力，也更接近成功。利用 Crunchbase 数据库进行的实证研究进一步表明，基于互联网的初创企业在投资领域占据重要地位，其中与金融和商业相关的初创企业不仅吸引了大量投资，还促进了相关初创企业的成功。
{"title":"Finding Successful Startups by Using Information Flows Among Investors in Higher Order Network of Investments","authors":"Wei Guan;Qing Guan;Yueran Duan;Changhong Xiang","doi":"10.1109/TCSS.2024.3394439","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3394439","url":null,"abstract":"Finding potential successful startups is always a key issue for industrial innovation and economic development, yet it poses a significant challenge due to the complexity of investments and low success rates. Compared with existing models on knowledge correlations among pairwise startups in a first-order perspective, potential dependencies among sequential investment behaviors reveal knowledge correlations among multiple startups, which requires modeling from a higher order perspective. In this article, a novel higher order network (HON) framework, generated by dependencies among investment behaviors with timestamps, is proposed to identify the pattern of knowledge flows among startups, which has been approved higher accuracy in predicting investment behaviors. Moreover, we introduce a HON-based centrality indicator to measure the importance of startups. Experiments compared with baseline models have shown that the startups identified by proposed indicator are more influential in knowledge propagation and are closer to success. An empirical study conducted by Crunchbase database further reveals that internet-based startups occupy a significant position in investment landscapes, with those associated with finance and commerce not only attracting considerable investments but also facilitating greater success for related startups.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5803-5814"},"PeriodicalIF":4.5,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368543","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}
Pub Date : 2024-06-07DOI: 10.1109/TCSS.2024.3399784
Ziqing Zhu;Guan Yuan;Tao Zhou;Jiuxin Cao
The community plays a crucial role in understanding user behavior and network characteristics in social networks. Some users can use multiple social networks at once for a variety of objectives. These users are called overlapping users who bridge different social networks. Detecting communities across multiple social networks is vital for interaction mining, information diffusion, and behavior migration analysis among networks. This article presents a community detection method based on nonnegative matrix trifactorization for multiple heterogeneous social networks, which formulates a common consensus matrix to represent the global fused community. Specifically, the proposed method involves creating adjacency matrices based on network structure and content similarity, followed by alignment matrices that distinguish overlapping users in different social networks. With the generated alignment matrices, the method could enhance the fusion degree of the global community by detecting overlapping user communities across networks. The effectiveness of the proposed method is evaluated with new metrics on Twitter, Instagram, and Tumblr datasets. The results of the experiments demonstrate its superior performance in terms of community quality and community fusion.
{"title":"Community Detection for Heterogeneous Multiple Social Networks","authors":"Ziqing Zhu;Guan Yuan;Tao Zhou;Jiuxin Cao","doi":"10.1109/TCSS.2024.3399784","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3399784","url":null,"abstract":"The community plays a crucial role in understanding user behavior and network characteristics in social networks. Some users can use multiple social networks at once for a variety of objectives. These users are called overlapping users who bridge different social networks. Detecting communities across multiple social networks is vital for interaction mining, information diffusion, and behavior migration analysis among networks. This article presents a community detection method based on nonnegative matrix trifactorization for multiple heterogeneous social networks, which formulates a common consensus matrix to represent the global fused community. Specifically, the proposed method involves creating adjacency matrices based on network structure and content similarity, followed by alignment matrices that distinguish overlapping users in different social networks. With the generated alignment matrices, the method could enhance the fusion degree of the global community by detecting overlapping user communities across networks. The effectiveness of the proposed method is evaluated with new metrics on Twitter, Instagram, and Tumblr datasets. The results of the experiments demonstrate its superior performance in terms of community quality and community fusion.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6966-6981"},"PeriodicalIF":4.5,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368276","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}
Pub Date : 2024-06-07DOI: 10.1109/TCSS.2024.3395794
Peican Zhu;Zechen Pan;Keke Tang;Xiaodong Cui;Jinhuan Wang;Qi Xuan
Graph neural network (GNN) has achieved remarkable success in various graph learning tasks, such as node classification, link prediction, and graph classification. The key to the success of GNN lies in its effective structure information representation through neighboring aggregation. However, the attacker can easily perturb the aggregation process through injecting fake nodes, which reveals that GNN is vulnerable to the graph injection attack (GIA). Existing GIA methods primarily focus on damaging the classical feature aggregation process while overlooking the neighborhood aggregation process via label propagation. To bridge this gap, we propose the label-propagation-based global injection attack (LPGIA) which conducts the GIA on the node classification task. Specifically, we analyze the aggregation process from the perspective of label propagation and transform the GIA problem into a global injection label specificity attack problem. To solve this problem, LPGIA utilizes a label-propagation-based strategy to optimize the combinations of the nodes connected to the injected node. Then, LPGIA leverages the feature mapping to generate malicious features for injected nodes. In extensive experiments against representative GNNs, LPGIA outperforms the previous best-performing injection attack method in various datasets, demonstrating its superiority and transferability.
图神经网络（GNN）在节点分类、链接预测和图分类等各种图学习任务中取得了显著的成功。图神经网络成功的关键在于其通过邻接聚合实现的有效结构信息表示。然而，攻击者可以通过注入假节点轻易扰乱聚合过程，这就揭示了 GNN 容易受到图注入攻击（GIA）。现有的 GIA 方法主要侧重于破坏经典的特征聚合过程，而忽略了通过标签传播的邻域聚合过程。为了弥补这一缺陷，我们提出了基于标签传播的全局注入攻击（LPGIA），它可以对节点分类任务进行 GIA。具体来说，我们从标签传播的角度分析了聚合过程，并将 GIA 问题转化为全局注入标签特异性攻击问题。为了解决这个问题，LPGIA 利用基于标签传播的策略来优化与注入节点相连的节点组合。然后，LPGIA 利用特征映射为注入节点生成恶意特征。在针对具有代表性的 GNN 进行的大量实验中，LPGIA 在各种数据集中的表现都优于之前表现最好的注入攻击方法，这证明了它的优越性和可移植性。
{"title":"Node Injection Attack Based on Label Propagation Against Graph Neural Network","authors":"Peican Zhu;Zechen Pan;Keke Tang;Xiaodong Cui;Jinhuan Wang;Qi Xuan","doi":"10.1109/TCSS.2024.3395794","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3395794","url":null,"abstract":"Graph neural network (GNN) has achieved remarkable success in various graph learning tasks, such as node classification, link prediction, and graph classification. The key to the success of GNN lies in its effective structure information representation through neighboring aggregation. However, the attacker can easily perturb the aggregation process through injecting fake nodes, which reveals that GNN is vulnerable to the graph injection attack (GIA). Existing GIA methods primarily focus on damaging the classical feature aggregation process while overlooking the neighborhood aggregation process via label propagation. To bridge this gap, we propose the label-propagation-based global injection attack (LPGIA) which conducts the GIA on the node classification task. Specifically, we analyze the aggregation process from the perspective of label propagation and transform the GIA problem into a global injection label specificity attack problem. To solve this problem, LPGIA utilizes a label-propagation-based strategy to optimize the combinations of the nodes connected to the injected node. Then, LPGIA leverages the feature mapping to generate malicious features for injected nodes. In extensive experiments against representative GNNs, LPGIA outperforms the previous best-performing injection attack method in various datasets, demonstrating its superiority and transferability.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5858-5870"},"PeriodicalIF":4.5,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368360","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}
Pub Date : 2024-06-06DOI: 10.1109/TCSS.2024.3398430
Tianjing Wang;Yu Zhang;Hang Shen;Guangwei Bai
Deep reinforcement learning (DRL) has become prevalent for decision-making task assignments in mobile crowdsensing (MCS). However, when facing sensing scenarios with varying numbers of workers or task attributes, existing DRL-based task assignment schemes fail to generate matching policies continuously and are susceptible to environmental fluctuations. To overcome these issues, a twin-delayed deep stochastic policy gradient (TDDS) approach is presented for balanced and low-latency MCS task decomposition and parallel subtask allocation. A masked attention mechanism is incorporated into the policy network to enable TDDS to adapt to task-attribute and subtask variations. To enhance environmental adaptability, an off-policy DRL algorithm incorporating experience replay is developed to eliminate sample correlation during training. Gumbel-Softmax sampling is integrated into the twin-delayed deep deterministic policy gradient (TD3) to support discrete action space decisions and a customized reward strategy to reduce task completion delay and balance workloads. Extensive simulation results confirm that the proposed scheme outperforms mainstream DRL baselines in terms of environmental adaptability, task completion delay, and workload balancing.
{"title":"Task Partitioning and Scheduling Based on Stochastic Policy Gradient in Mobile Crowdsensing","authors":"Tianjing Wang;Yu Zhang;Hang Shen;Guangwei Bai","doi":"10.1109/TCSS.2024.3398430","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3398430","url":null,"abstract":"Deep reinforcement learning (DRL) has become prevalent for decision-making task assignments in mobile crowdsensing (MCS). However, when facing sensing scenarios with varying numbers of workers or task attributes, existing DRL-based task assignment schemes fail to generate matching policies continuously and are susceptible to environmental fluctuations. To overcome these issues, a twin-delayed deep stochastic policy gradient (TDDS) approach is presented for balanced and low-latency MCS task decomposition and parallel subtask allocation. A masked attention mechanism is incorporated into the policy network to enable TDDS to adapt to task-attribute and subtask variations. To enhance environmental adaptability, an off-policy DRL algorithm incorporating experience replay is developed to eliminate sample correlation during training. Gumbel-Softmax sampling is integrated into the twin-delayed deep deterministic policy gradient (TD3) to support discrete action space decisions and a customized reward strategy to reduce task completion delay and balance workloads. Extensive simulation results confirm that the proposed scheme outperforms mainstream DRL baselines in terms of environmental adaptability, task completion delay, and workload balancing.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6580-6591"},"PeriodicalIF":4.5,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368371","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}
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