Pub Date : 2024-06-21DOI: 10.1109/TCSS.2024.3409383
Junwei Li;Le Wu;Yulu Du;Richang Hong;Weisheng Li
Social network services (SNSs) provide platforms where users engage in social link behavior (e.g., predicting social relationships) and consumption behavior. Recent advancements in deep learning for recommendation and link prediction explore the symbiotic relationships between these behaviors, leveraging social influence theory and user homogeneity, i.e., users tend to accept recommendations from social friends and connect with like-minded users. These studies yield positive feedback for users and platforms, fostering practical applications and economic development. While previous works jointly model these behaviors, most studies often overlook the evolution of social relationships and users’ preferences in dynamic scenes and the correlations inside, as well as the higher order information within the social network and preference network (consumption history). To address this, we propose the dynamic graph neural joint behavior prediction model (DGN-JBP). Specifically, we actively disentangle and initialize user embeddings from multiple perspectives to refine information for modeling. Additionally, we design an attentive graph neural network and combine it with gate recurrent units (GRUs) to extract high-order dynamic information. Finally, we design a dual framework and purposefully fuse embeddings to mutually enhance the effectiveness of predictions on two prediction tasks. Extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of our proposed model.
{"title":"Dual Graph Neural Networks for Dynamic Users’ Behavior Prediction on Social Networking Services","authors":"Junwei Li;Le Wu;Yulu Du;Richang Hong;Weisheng Li","doi":"10.1109/TCSS.2024.3409383","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3409383","url":null,"abstract":"Social network services (SNSs) provide platforms where users engage in social link behavior (e.g., predicting social relationships) and consumption behavior. Recent advancements in deep learning for recommendation and link prediction explore the symbiotic relationships between these behaviors, leveraging social influence theory and user homogeneity, i.e., users tend to accept recommendations from social friends and connect with like-minded users. These studies yield positive feedback for users and platforms, fostering practical applications and economic development. While previous works jointly model these behaviors, most studies often overlook the evolution of social relationships and users’ preferences in dynamic scenes and the correlations inside, as well as the higher order information within the social network and preference network (consumption history). To address this, we propose the dynamic graph neural joint behavior prediction model (DGN-JBP). Specifically, we actively disentangle and initialize user embeddings from multiple perspectives to refine information for modeling. Additionally, we design an attentive graph neural network and combine it with gate recurrent units (GRUs) to extract high-order dynamic information. Finally, we design a dual framework and purposefully fuse embeddings to mutually enhance the effectiveness of predictions on two prediction tasks. Extensive experimental results on two real-world datasets clearly demonstrate the effectiveness of our proposed model.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"7020-7031"},"PeriodicalIF":4.5,"publicationDate":"2024-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368357","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 : 2024-06-20DOI: 10.1109/TCSS.2024.3390931
Syed Shafat Ali;Ajay Rastogi;Tarique Anwar
We often see rumors rapidly spreading in online social networks. These are harmful for our society in many ways. Infection source detection is the task of identifying the sources of rumors or any other such infections in social networks, so that appropriate intervention could be performed to control the harm. Researchers have studied this problem under various scenarios, where multisource detection has been of special importance. In this article, we propose a novel infection rate controlled label propagation method for multisource detection called FROST . It leverages the connection strengths between a pair of nodes in the form of infection rate to capture the implicit information latent within an infection. Initially, labels are assigned to nodes indicating whether the nodes are infected or not. Afterward, the labels are propagated across the network in a controlled manner based on the infection rate. Once the propagation converges, the locally prominent nodes are considered as sources. We compare FROST against six state-of-the-art methods and two heuristic baselines in terms of ten evaluation measures over four social networks datasets. Our results show that FROST generally outperforms the competing methods across various evaluation measures and datasets. It also estimates the number of sources closer to the actual than the competing methods. FROST scales effectively for large infections, including when there are infection overlaps, where the competing methods generally lag.
{"title":"FROST: Controlled Label Propagation for Multisource Detection","authors":"Syed Shafat Ali;Ajay Rastogi;Tarique Anwar","doi":"10.1109/TCSS.2024.3390931","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3390931","url":null,"abstract":"We often see rumors rapidly spreading in online social networks. These are harmful for our society in many ways. Infection source detection is the task of identifying the sources of rumors or any other such infections in social networks, so that appropriate intervention could be performed to control the harm. Researchers have studied this problem under various scenarios, where multisource detection has been of special importance. In this article, we propose a novel infection rate controlled label propagation method for multisource detection called \u0000<monospace>FROST</monospace>\u0000. It leverages the connection strengths between a pair of nodes in the form of infection rate to capture the implicit information latent within an infection. Initially, labels are assigned to nodes indicating whether the nodes are infected or not. Afterward, the labels are propagated across the network in a controlled manner based on the infection rate. Once the propagation converges, the locally prominent nodes are considered as sources. We compare \u0000<monospace>FROST</monospace>\u0000 against six state-of-the-art methods and two heuristic baselines in terms of ten evaluation measures over four social networks datasets. Our results show that \u0000<monospace>FROST</monospace>\u0000 generally outperforms the competing methods across various evaluation measures and datasets. It also estimates the number of sources closer to the actual than the competing methods. \u0000<monospace>FROST</monospace>\u0000 scales effectively for large infections, including when there are infection overlaps, where the competing methods generally lag.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6217-6228"},"PeriodicalIF":4.5,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368365","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 : 2024-06-20DOI: 10.1109/TCSS.2024.3398696
Haojie Liu;Hao Luo;Xiantao Peng;Wei Jiang
Visible–infrared person reidentification (VI-ReID) in social surveillance systems involves analyzing social behavior using nonoverlapping cross-modality camera sets. It often has poor retrieval performance under modality gap. One way to alleviate such the modality discrepancy is to learn shared person features that are generalizable across different modalities. However, because of significant differences in color between the visible and infrared images, the learned share features are always inclined to specific information of corresponding modality. To this end, we propose a modality bias calibration network (MBCNet) that filters out identity-irrelevant interference and recalibrates the learned modality-shared features. Specifically, to emphasize the modality-shared cues, we employ a feature decomposition module in the feature-level to filter out style variations and extract identity-relevant discriminative cues from the residual feature. In order to achieve a better disentanglement, a dual ranking entropy constraint is further proposed to ensure that the learned features contain only identity-relevant information and discard style-relevant information. Simultaneously, we design a decorrelated orthogonality Loss to ensure the disentangled features are not correlated with each other. Through comprehensive experiments, we demonstrate that MBCNet significantly improves the cross-modality retrieval performance in social surveillance systems and effectively addresses the modality bias training issue.
{"title":"Modality Bias Calibration Network via Information Disentanglement for Visible–Infrared Person Reidentification","authors":"Haojie Liu;Hao Luo;Xiantao Peng;Wei Jiang","doi":"10.1109/TCSS.2024.3398696","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3398696","url":null,"abstract":"Visible–infrared person reidentification (VI-ReID) in social surveillance systems involves analyzing social behavior using nonoverlapping cross-modality camera sets. It often has poor retrieval performance under modality gap. One way to alleviate such the modality discrepancy is to learn shared person features that are generalizable across different modalities. However, because of significant differences in color between the visible and infrared images, the learned share features are always inclined to specific information of corresponding modality. To this end, we propose a modality bias calibration network (MBCNet) that filters out identity-irrelevant interference and recalibrates the learned modality-shared features. Specifically, to emphasize the modality-shared cues, we employ a feature decomposition module in the feature-level to filter out style variations and extract identity-relevant discriminative cues from the residual feature. In order to achieve a better disentanglement, a dual ranking entropy constraint is further proposed to ensure that the learned features contain only identity-relevant information and discard style-relevant information. Simultaneously, we design a decorrelated orthogonality Loss to ensure the disentangled features are not correlated with each other. Through comprehensive experiments, we demonstrate that MBCNet significantly improves the cross-modality retrieval performance in social surveillance systems and effectively addresses the modality bias training issue.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6925-6938"},"PeriodicalIF":4.5,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368422","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 : 2024-06-19DOI: 10.1109/TCSS.2024.3404236
Yeaeun Gong;Lanyu Shang;Dong Wang
This article overviews the state of the art, research challenges, and future directions in our vision: integrating social explanation into explainable artificial intelligence (XAI) to combat misinformation. In our context, “social explanation” is an explanatory approach that reveals the social aspect of misinformation by analyzing sociocontextual cues, such as user attributes, user engagement metrics, diffusion patterns, and user comments. Our vision is motivated by the research gap in the existing XAI that tends to overlook the broader social context in which misinformation spreads. In this article, we first define social explanation, demonstrating it through examples, enabling technologies, and real-world applications. We then outline the unique benefits social explanation brings to the fight against misinformation and discuss the challenges that make our vision complex. The significance of this article lies in introducing the “social explanation” concept in XAI, which has been underexplored in the previous literature. Also, we demonstrate how social explanations can be effectively employed to tackle misinformation and promote collaboration across diverse fields by drawing upon interdisciplinary techniques spanning from computer science, social computing, human–computer interaction, to psychology. We hope that this article will advance progress in the field of XAI and contribute to the ongoing efforts to counter misinformation.
{"title":"Integrating Social Explanations Into Explainable Artificial Intelligence (XAI) for Combating Misinformation: Vision and Challenges","authors":"Yeaeun Gong;Lanyu Shang;Dong Wang","doi":"10.1109/TCSS.2024.3404236","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3404236","url":null,"abstract":"This article overviews the state of the art, research challenges, and future directions in our vision: integrating social explanation into explainable artificial intelligence (XAI) to combat misinformation. In our context, “social explanation” is an explanatory approach that reveals the social aspect of misinformation by analyzing sociocontextual cues, such as user attributes, user engagement metrics, diffusion patterns, and user comments. Our vision is motivated by the research gap in the existing XAI that tends to overlook the broader social context in which misinformation spreads. In this article, we first define social explanation, demonstrating it through examples, enabling technologies, and real-world applications. We then outline the unique benefits social explanation brings to the fight against misinformation and discuss the challenges that make our vision complex. The significance of this article lies in introducing the “social explanation” concept in XAI, which has been underexplored in the previous literature. Also, we demonstrate how social explanations can be effectively employed to tackle misinformation and promote collaboration across diverse fields by drawing upon interdisciplinary techniques spanning from computer science, social computing, human–computer interaction, to psychology. We hope that this article will advance progress in the field of XAI and contribute to the ongoing efforts to counter misinformation.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6705-6726"},"PeriodicalIF":4.5,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10565780","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368532","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 : 2024-06-19DOI: 10.1109/TCSS.2024.3408214
Jianrui Chen;Meixia He;Peican Zhu;Zhihui Wang
Recently, higher order patterns have played an important role in network structure analysis. The simplices in higher order patterns enrich dynamic network modeling and provide strong structural feature information for feature learning. However, the disorder dynamic network with simplex patterns has not been organized and divided according to time windows. Besides, existing methods do not make full use of the feature information to predict the simplex patterns with higher orders. To address these issues, we propose a simplex pattern prediction method based on dynamic higher order path convolutional networks. First, we divide the dynamic higher order datasets into different network structures under continuous-time windows, which possess complete time information. Second, feature extraction is performed on the network structure of continuous-time windows through higher order path convolutional networks. Subsequently, we embed time nodes into feature encoding and obtain feature representations of simplex patterns through feature fusion. The obtained feature representations of simplices are recognized by a simplex pattern discriminator to predict the simplex patterns at different moments. Finally, compared to other dynamic graph representation learning algorithms, our proposed algorithm has significantly improved its performance in predicting simplex patterns on five real dynamic higher order datasets.
{"title":"Simplex Pattern Prediction Based on Dynamic Higher Order Path Convolutional Networks","authors":"Jianrui Chen;Meixia He;Peican Zhu;Zhihui Wang","doi":"10.1109/TCSS.2024.3408214","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3408214","url":null,"abstract":"Recently, higher order patterns have played an important role in network structure analysis. The simplices in higher order patterns enrich dynamic network modeling and provide strong structural feature information for feature learning. However, the disorder dynamic network with simplex patterns has not been organized and divided according to time windows. Besides, existing methods do not make full use of the feature information to predict the simplex patterns with higher orders. To address these issues, we propose a simplex pattern prediction method based on dynamic higher order path convolutional networks. First, we divide the dynamic higher order datasets into different network structures under continuous-time windows, which possess complete time information. Second, feature extraction is performed on the network structure of continuous-time windows through higher order path convolutional networks. Subsequently, we embed time nodes into feature encoding and obtain feature representations of simplex patterns through feature fusion. The obtained feature representations of simplices are recognized by a simplex pattern discriminator to predict the simplex patterns at different moments. Finally, compared to other dynamic graph representation learning algorithms, our proposed algorithm has significantly improved its performance in predicting simplex patterns on five real dynamic higher order datasets.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6623-6636"},"PeriodicalIF":4.5,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368518","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 : 2024-06-17DOI: 10.1109/TCSS.2024.3395627
Liman Du;Wenguo Yang;Suixiang Gao
To provide a new perspective for understanding the dynamics of advertising campaigns in online influencer marketing (OIM), this article proposes the modified agent-based (MAB) model. As an extension of the existing agent-based model, it takes into account some important factors such as influencer avoidance and product competition and modifies some existing parameters to provide a more realistic approach for simulating marketing campaigns. Based on it, we define a novel set function named as dual-profit function. It is proven to be nonsubmodular and nonsupermodular. The dual-profit maximization (DPM) problem which regards dual-profit function as its objective function and the DPM algorithm used to address DPM problem is proposed. The influence of several parameters is evaluated through experiments conducted on real-world datasets.
{"title":"Profit Maximization in Online Influencer Marketing From the Perspective of Modified Agent-Based Modeling","authors":"Liman Du;Wenguo Yang;Suixiang Gao","doi":"10.1109/TCSS.2024.3395627","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3395627","url":null,"abstract":"To provide a new perspective for understanding the dynamics of advertising campaigns in online influencer marketing (OIM), this article proposes the modified agent-based (MAB) model. As an extension of the existing agent-based model, it takes into account some important factors such as influencer avoidance and product competition and modifies some existing parameters to provide a more realistic approach for simulating marketing campaigns. Based on it, we define a novel set function named as dual-profit function. It is proven to be nonsubmodular and nonsupermodular. The dual-profit maximization (DPM) problem which regards dual-profit function as its objective function and the DPM algorithm used to address DPM problem is proposed. The influence of several parameters is evaluated through experiments conducted on real-world datasets.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6501-6512"},"PeriodicalIF":4.5,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368334","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 : 2024-06-17DOI: 10.1109/TCSS.2024.3406528
Mengli Wei;Ju Yang;Zhongyuan Zhao;Xiaogang Zhang;Jinsong Li;Zhiliang Deng
Heart disease is a leading global cause of death, while federated learning (FL) is an effective way to predict it. Due to patient privacy concerns and the centralized nature of current FL approaches, collaborative research in heart disease prediction (HDP) faces significant hurdles in computational health systems. This article introduces a distributed online aggregation method within fully decentralized federated learning (DFL), named DeFedHDP, to address these privacy challenges and improve the model of HDP. Moreover, the differential privacy (DP) mechanism is applied to the aggregation strategy of DeFedHDP to protect the privacy of the patients. The data holder communicates directly with neighbors in a series of time-varying directed graphs without the involvement of a central server. Furthermore, each participant is both a trainer of the local model and a collaborator of the other participants’ models. The data does not leave the local device, only the model parameters are exchanged and integrated, and this decentralized approach can further improve the level of privacy protection. In addition, to cope with model gradient disappearance and gradient explosion, the one-point bandit feedback (OPBF) strategy is utilized to estimate the true gradient values. Experiments on a public medical dataset show that the effectiveness of DeFedHDP is close to the centralized FedAVG algorithm for client-server architectures in terms of accuracy and speed.
{"title":"DeFedHDP: Fully Decentralized Online Federated Learning for Heart Disease Prediction in Computational Health Systems","authors":"Mengli Wei;Ju Yang;Zhongyuan Zhao;Xiaogang Zhang;Jinsong Li;Zhiliang Deng","doi":"10.1109/TCSS.2024.3406528","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3406528","url":null,"abstract":"Heart disease is a leading global cause of death, while federated learning (FL) is an effective way to predict it. Due to patient privacy concerns and the centralized nature of current FL approaches, collaborative research in heart disease prediction (HDP) faces significant hurdles in computational health systems. This article introduces a distributed online aggregation method within fully decentralized federated learning (DFL), named DeFedHDP, to address these privacy challenges and improve the model of HDP. Moreover, the differential privacy (DP) mechanism is applied to the aggregation strategy of DeFedHDP to protect the privacy of the patients. The data holder communicates directly with neighbors in a series of time-varying directed graphs without the involvement of a central server. Furthermore, each participant is both a trainer of the local model and a collaborator of the other participants’ models. The data does not leave the local device, only the model parameters are exchanged and integrated, and this decentralized approach can further improve the level of privacy protection. In addition, to cope with model gradient disappearance and gradient explosion, the one-point bandit feedback (OPBF) strategy is utilized to estimate the true gradient values. Experiments on a public medical dataset show that the effectiveness of DeFedHDP is close to the centralized FedAVG algorithm for client-server architectures in terms of accuracy and speed.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6854-6867"},"PeriodicalIF":4.5,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368444","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}
Bitcoin is a cryptocurrency designed based on the concept of “decentralization,” and its price fluctuation is much larger than those of traditional financial assets, which has raised concerns about intentioned Bitcoin price manipulation. Most of the existing research on Bitcoin price manipulation is limited to the analysis of manipulation behaviors, lacking effective identification methods and performance metrics of identification methods. In this article, we use MtGox real trading data to build a trading network and analyze the diverse price manipulation patterns in the trading network. Based on this, we improve the classical graph representation learning method to effectively identify the price manipulation accounts. Specifically, considering that Bitcoin has special financial investment properties, and its price condenses important information from the financial market, we propose a novel identification algorithm, called Finan2vec, by feeding the time-series information of the price into the transfer strategy of the Node2vec algorithm. This algorithm makes it easier to detect nodes with large trades or significant price-raising behavior. We then use two classification algorithms to complete the identification of abnormal nodes and finally draw on the knowledge of financial market experts to construct a hit rate indicator to measure the effectiveness of the proposed method. The experimental results show that our method can effectively identify price manipulation accounts for Bitcoin and other public blockchain systems.
{"title":"A Novel Method for Bitcoin Price Manipulation Identification Based on Graph Representation Learning","authors":"Yanmei Zhang;Ziyu Li;Yuwen Su;Jianjun Li;Shiping Chen","doi":"10.1109/TCSS.2024.3368690","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3368690","url":null,"abstract":"Bitcoin is a cryptocurrency designed based on the concept of “decentralization,” and its price fluctuation is much larger than those of traditional financial assets, which has raised concerns about intentioned Bitcoin price manipulation. Most of the existing research on Bitcoin price manipulation is limited to the analysis of manipulation behaviors, lacking effective identification methods and performance metrics of identification methods. In this article, we use MtGox real trading data to build a trading network and analyze the diverse price manipulation patterns in the trading network. Based on this, we improve the classical graph representation learning method to effectively identify the price manipulation accounts. Specifically, considering that Bitcoin has special financial investment properties, and its price condenses important information from the financial market, we propose a novel identification algorithm, called Finan2vec, by feeding the time-series information of the price into the transfer strategy of the Node2vec algorithm. This algorithm makes it easier to detect nodes with large trades or significant price-raising behavior. We then use two classification algorithms to complete the identification of abnormal nodes and finally draw on the knowledge of financial market experts to construct a hit rate indicator to measure the effectiveness of the proposed method. The experimental results show that our method can effectively identify price manipulation accounts for Bitcoin and other public blockchain systems.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5607-5618"},"PeriodicalIF":4.5,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368362","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 : 2024-06-17DOI: 10.1109/TCSS.2024.3406629
Xiaobo Chen;Kaiyuan Wang;Feng Zhao;Fuwen Deng;Qiaolin Ye
Traffic sensor networks allow convenient collection of travel data that are of great significance for intelligent transportation systems (ITSs). However, the universality of missing data impedes the application of ITS and thus accurate missing data recovery is indispensable in practice. Typically, the global low-rankness and local spatiotemporal smoothness exist in underlying traffic tensor data. In light of this, this article proposes an improved low-rank tensor completion (LRTC) model by exploiting abundant structural information from incomplete tensors. Specifically, a logarithm power composite (LPC)-norm is first proposed as a nonconvex substitute of the rank function, leading to a flexible characterization of tensor multidimensional correlation. Then, a joint structural regression (JSR) model is presented to simultaneously leverage the intrinsic temporal continuity and profile similarity of traffic data. By doing so, we construct a novel nonconvex LRTC model by integrating the global low-rankness and fine-grained spatiotemporal structure that are complementary to each other. To solve the proposed model, following the optimization framework of the alternating direction method of multipliers (ADMMs), we develop an efficient iterative algorithm where each step can be solved in a closed form. Extensive experiments on four real-world traffic data are conducted to evaluate the effectiveness of the proposed approach. The results demonstrate that compared with other tensor completion methods, our model significantly improves the recovery performance.
{"title":"Composite Nonconvex Low-Rank Tensor Completion With Joint Structural Regression for Traffic Sensor Networks Data Recovery","authors":"Xiaobo Chen;Kaiyuan Wang;Feng Zhao;Fuwen Deng;Qiaolin Ye","doi":"10.1109/TCSS.2024.3406629","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3406629","url":null,"abstract":"Traffic sensor networks allow convenient collection of travel data that are of great significance for intelligent transportation systems (ITSs). However, the universality of missing data impedes the application of ITS and thus accurate missing data recovery is indispensable in practice. Typically, the global low-rankness and local spatiotemporal smoothness exist in underlying traffic tensor data. In light of this, this article proposes an improved low-rank tensor completion (LRTC) model by exploiting abundant structural information from incomplete tensors. Specifically, a logarithm power composite (LPC)-norm is first proposed as a nonconvex substitute of the rank function, leading to a flexible characterization of tensor multidimensional correlation. Then, a joint structural regression (JSR) model is presented to simultaneously leverage the intrinsic temporal continuity and profile similarity of traffic data. By doing so, we construct a novel nonconvex LRTC model by integrating the global low-rankness and fine-grained spatiotemporal structure that are complementary to each other. To solve the proposed model, following the optimization framework of the alternating direction method of multipliers (ADMMs), we develop an efficient iterative algorithm where each step can be solved in a closed form. Extensive experiments on four real-world traffic data are conducted to evaluate the effectiveness of the proposed approach. The results demonstrate that compared with other tensor completion methods, our model significantly improves the recovery performance.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6882-6896"},"PeriodicalIF":4.5,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368275","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 : 2024-06-14DOI: 10.1109/TCSS.2024.3404239
Xuming An;Chen Zhang;Lin Hou;Kaibo Wang
Acknowledging the significance of information propagation and individual adaptive behavior has been regarded as an indispensable prerequisite for a complete understanding of epidemic spreading. Recent studies have widely considered the metapopulation model, where epidemics spread over a single layer of physical networks via individual mobility. However, these advances neglected the interventions of accompanied information and individual behavior response related to epidemics. In this article, we develop a coupled epidemic-information propagation model on multiplex metapopulation networks leveraging the microscopic Markov chain (MMC) approach, aiming to explore the spatiotemporal characteristics of epidemic spreading process. Taking the individual adaptive behavior into account, the stranding mechanism based on infection level and medical resources is introduced to capture the population size dynamics during individual mobility among different patches. Theoretical epidemic threshold is analytically derived under the improved framework. Extensive numerical simulations are performed to validate our theoretical analysis and further examine the impacts of information propagation and spreading parameters on epidemic threshold and steady-state prevalence. Our results indicate that both the scale of information diffusion and the specific configuration of spreading parameters can significantly suppress the epidemic prevalence. These findings shed a novel light on theoretical research and decision-making of coupled epidemic-information process in the spatiotemporal perspective.
{"title":"Coupled Epidemic-Information Propagation With Stranding Mechanism on Multiplex Metapopulation Networks","authors":"Xuming An;Chen Zhang;Lin Hou;Kaibo Wang","doi":"10.1109/TCSS.2024.3404239","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3404239","url":null,"abstract":"Acknowledging the significance of information propagation and individual adaptive behavior has been regarded as an indispensable prerequisite for a complete understanding of epidemic spreading. Recent studies have widely considered the metapopulation model, where epidemics spread over a single layer of physical networks via individual mobility. However, these advances neglected the interventions of accompanied information and individual behavior response related to epidemics. In this article, we develop a coupled epidemic-information propagation model on multiplex metapopulation networks leveraging the microscopic Markov chain (MMC) approach, aiming to explore the spatiotemporal characteristics of epidemic spreading process. Taking the individual adaptive behavior into account, the stranding mechanism based on infection level and medical resources is introduced to capture the population size dynamics during individual mobility among different patches. Theoretical epidemic threshold is analytically derived under the improved framework. Extensive numerical simulations are performed to validate our theoretical analysis and further examine the impacts of information propagation and spreading parameters on epidemic threshold and steady-state prevalence. Our results indicate that both the scale of information diffusion and the specific configuration of spreading parameters can significantly suppress the epidemic prevalence. These findings shed a novel light on theoretical research and decision-making of coupled epidemic-information process in the spatiotemporal perspective.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"6727-6744"},"PeriodicalIF":4.5,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368415","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：