IEEE Transactions on Big Data最新文献

{"title":"Attention-Based Complex Logical Query on Temporal Knowledge Graph via Graph Neural Network","authors":"Luyi Bai;Linshuo Xu;Lin Zhu","doi":"10.1109/TBDATA.2024.3489421","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3489421","url":null,"abstract":"Answering complex logical queries on large-scale Knowledge Graphs (KGs) efficiently and accurately has always been crucial for question-answering systems. Recent studies have significantly improved the performance of complex logical queries on massive knowledge graphs by leveraging graph neural networks (GNNs). However, the existing GNN-based methods still have limitations in dealing with long-sequence logical queries. They usually decompose complex queries into multiple independent first-order logical queries, which leads to the inability to optimize globally, and the query accuracy will drop sharply with the increase of query length. In addition, the knowlege in the real world is dynamically changing, but most of the existing methods are more suitable for dealing with static knowledge graphs, and there is still much room for improvement when dealing with logical queries in temporal knowledge graphs. In this paper, we propose a novel Temporal Complex Logical Query (TCLQ) model to achieve temporal logical queries on temporal knowledge graphs. We add time series embedding into GNN, and use multi-layer GRUs to aggregate the node features of previous time and current time, which effectively enhances the time series reasoning ability of the model. In order to solve the problem that the accuracy of logical query model decreases significantly with the increase of query sequence length, we establish a multi-level attention coefficients model to learn and optimize the whole logical queries, thus reducing the error accumulation problem when the queries are decomposed into multiple independent first-order logical queries. We conduct experiments on multiple temporal datasets and demonstrate the effectiveness of TCLQ.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1828-1839"},"PeriodicalIF":7.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DGNN: Decoupled Graph Neural Networks With Structural Consistency Between Attribute and Graph Embedding Representations","authors":"Jinlu Wang;Jipeng Guo;Yanfeng Sun;Junbin Gao;Shaofan Wang;Yachao Yang;Baocai Yin","doi":"10.1109/TBDATA.2024.3489420","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3489420","url":null,"abstract":"Graph neural networks (GNNs) exhibit a robust capability for representation learning on graphs with complex structures, demonstrating superior performance across various applications. Most existing GNNs utilize graph convolution operations that integrate both attribute and structural information through coupled way. And these GNNs, from an optimization perspective, seek to learn a consensus and compromised embedding representation that balances attribute and graph information, selectively exploring and retaining valid information in essence. To obtain a more comprehensive embedding representation, a novel GNN framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced. DGNN separately explores distinctive embedding representations from the attribute and graph spaces by decoupled terms. Considering that the semantic graph, derived from attribute feature space, contains different node connection information and provides enhancement for the topological graph, both topological and semantic graphs are integrated by DGNN for powerful embedding representation learning. Further, structural consistency between the attribute embedding and the graph embedding is promoted to effectively eliminate redundant information and establish soft connection. This process involves facilitating factor sharing for adjacency matrices reconstruction, which aims at exploring consensus and high-level correlations. Finally, a more powerful and comprehensive representation is achieved through the concatenation of these embeddings. Experimental results conducted on several graph benchmark datasets demonstrate its superiority in node classification tasks.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1813-1827"},"PeriodicalIF":7.5,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AFML: An Asynchronous Federated Meta-Learning Mechanism for Charging Station Occupancy Prediction With Biased and Isolated Data","authors":"Qiyang Chen;Linlin You;Haohao Qu;Ahmed M. Abdelmoniem;Chau Yuen","doi":"10.1109/TBDATA.2024.3484651","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3484651","url":null,"abstract":"Electric vehicles (EVs) are driving green and low-carbon transport in modern cities. It makes charging station occupancy prediction (CSOP) critual for intelligent transportation systems (ITS) to achieve a balance between the supply and demand in resolving the dynamics between EVs and changing stations. Even though several Big Data-based solutions have been discussed, they are still struggling to collaboratively utilize heterogeneous data and distributed computing resources located at both physically and logicially isolated charging stations to better support context-driven CSOP. To addres this challenge, we propose an Asynchronous Federated Meta-learning Mechanism (AFML) for CSOP, which can train a meta-model with strong adaptation ability in an asynchronous and collaborative manner. In general, it incorporates an adaptive reptile algorithm (AR) and an weighted aggregation strategy (WA) to jointly ensure the training efficiency and model adaptivity. Evaluations on real-world CSOP datasets demonstrate that compared to the second best method, AFML can significantly improve forecasting accuracy by 14%, accelerate model convergence by 9% and enhance model generalizability by 10%, illustrating its merits in support CSOP to embrace a smart and sustainable city.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1772-1786"},"PeriodicalIF":7.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boosting Encrypted Traffic Classification Using Feature-Enhanced Recurrent Neural Network With Angle Constraint","authors":"Gongxun Miao;Guohua Wu;Zhen Zhang;Yongjie Tong;Bing Lu","doi":"10.1109/TBDATA.2024.3484674","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3484674","url":null,"abstract":"With the surge in various types of network traffic and the widespread application of encryption technology, the classification of encrypted traffic plays an increasingly important role in ensuring network security, enhancing quality of service, and managing network traffic. However, most existing methods often suffer from issues such as excessive reliance on manual feature extraction and expert knowledge, unstable classification performance, and lack of transfer learning capabilities. To address these challenges, this paper proposes a high-performance hybrid encrypted traffic classification framework, FERNN-AC. It directly extracts features from raw traffic and fully explores and utilizes the spatiotemporal information of traffic data by integrating specially designed feature enhancement module and temporal feature extraction module in a reasonable manner. It introduces angle constraints and can be combined with meta-learning, thereby improving classification performance while possessing certain transfer learning capabilities. The experiments are conducted on three datasets, and the results shows that compare with relevant baseline methods, FERNN-AC has excellent and stable classification performance.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1760-1771"},"PeriodicalIF":7.5,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LBFL: A Lightweight Blockchain-Based Federated Learning Framework With Proof-of-Contribution Committee Consensus","authors":"Shaojie Qiao;Yuhe Jiang;Nan Han;Wei Hua;Yufeng Lin;Shengjie Min;Xindong Wu","doi":"10.1109/TBDATA.2024.3481952","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3481952","url":null,"abstract":"Blockchain technology makes it possible to design robust decentralized federated learning (FL). Minimizing the communication cost and storage consumption incurred is one of the essential challenges. In addition, maintaining the security and privacy of Big Data raises to be a difficult problem. Aiming to tackle these challenges, this paper presents LBFL (a <bold>L</b>ightweight <bold>B</b>lockchain-based <bold>FL</b> framework) that offers three novel features. First, it employs a new committee consensus mechanism called Proof-of-Contribution, which is used to avoid the selection latency from the competition of miners and alleviate the congestion in cross-validation of parameters in an asynchronous fashion. Second, LBFL employs a role-adaptive incentive mechanism to estimate devices’ workloads and identify malicious nodes effectively. Third, to cope with the excessive storage overheads incurred in full-replication, LBFL applies a new storage partition mechanism that distributes triple redundant chunks in Reed-Solomon coding (RSC) evenly to participating devices with high fault tolerance and recovery efficiency. To evaluate LBFL, empirical studies are performed on the famous <italic>MNIST</i> dataset and LBFL is compared with the state-of-the-art FL frameworks. The results demonstrate that LBFL can reduce evaluation latency and storage consumption by 69.2% and 72.1%, respectively, and the learning efficiency of LBFL is higher than the state-of-the-art methods. In particular, important findings are obtained: the proposed role-adaptive incentive mechanism can properly identify malicious devices and switch the roles of legitimate devices to achieve good decentralization.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1745-1759"},"PeriodicalIF":7.5,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Knowledge-Guided Event-Relation Graph Learning Network for Patient Similarity With Chinese Electronic Medical Records","authors":"Zhichao Zhu;Jianqiang Li;Chun Xu;Jingchen Zou;Qing Zhao","doi":"10.1109/TBDATA.2024.3481955","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3481955","url":null,"abstract":"Feature sparse problem is commonly existing in patient similarity calculation task with clinical data, to track which, some approaches have been proposed to use Graph Neural Network (GNN) to model the complex structural information in patient Electronic Medical Records (EMRs). These GNN based approaches usually treat medical concepts (i.e., symptoms, diseases) as nodes to learn spatial features and adopt Recurrent Neural Network (RNN) to learn temporal sequence of these concepts. However, in many cases, several sequential concepts contained in EMR text are considered as occur simultaneously in the clinical diagnosis (i.e., some symptoms are detected simultaneously by once test), learning temporal sequence of these sequential concepts might cause noise for patient similarity calculation. Furthermore, the limited discriminative capability of concepts cannot provide sufficient indicative information for similarity learning. To this end, we propose a Knowledge-guided Event-relation Graph Learning Network (KEGLN) for patient similarity calculation. Specifically, after event extraction, we first construct element-relation graphs and use the first Graph Convolutional Network (GCN) and Graph Attention Network (GAT) layer to aggregate features from each event and its involved elements for reducing the noise produced by temporal sequence of concepts. Meanwhile, the entity description and attribute-value structure are extracted to supplement background knowledge of elements (concepts and trigger words). For the updated event nodes, we then design a event-relation graph and adopt the second GCN and GAT layer to aggregate information from events and their directly neighbors to extract spatial features of events at the current moment. Finally, the Bidirectional Long Short-Term Memory (BiLSTM) model is adopted to learn temporal dependency of event nodes to capture the dynamic change of disease progress. Through diverse datasets and extensive experiments, our KEGLN model outperforms all baselines for Chinese patient similarity calculation.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1475-1492"},"PeriodicalIF":7.5,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards Enhancing Inter-Domain Routing Security With Visualization and Visual Analytics","authors":"Jingwei Tang;Guodao Sun;Jiahui Chen;Gefei Zhang;Qi Jiang;Yanbiao Li;Guangxing Zhang;Jian Liu;Haixia Wang;Ronghua Liang","doi":"10.1109/TBDATA.2024.3481899","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3481899","url":null,"abstract":"In the complex landscape of the Internet, inter-domain routing systems are essential for ensuring seamless connectivity and reachability across autonomous systems. However, the lack of dependable security validation mechanisms in these systems poses persistent challenges. Vulnerabilities such as prefix hijacking, path forgery, and route leakage not only compromise network operators and users, but also threaten the stability and accessibility of the Internet’s core infrastructure. To address this, visualization and visual analytics techniques are adept at identifying and detecting security threats, offering network administrators effective methods to monitor and maintain network operations. This paper presents a comprehensive survey of the state-of-the-art research in visualization and visual analytics for inter-domain routing security. We delineate four scenarios for tasks analysis in network visualization: monitoring, detection, verification, and discovery. Each category is explored in detail, focusing on the employed data sources and visualization techniques. Several key findings are presented at the end of each category, aimed at providing researchers and practitioners with research inspiration. Furthermore, we examine the trends of academic interest observed in recent decades and propose potential directions for future research in visual analytics pertaining to Internet infrastructure security.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1508-1527"},"PeriodicalIF":7.5,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Residual Coupled Prompt Learning for Zero-Shot Sketch-Based Image Retrieval","authors":"Guangyao Zhuo;Zhenqiu Shu;Zhengtao Yu","doi":"10.1109/TBDATA.2024.3481898","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3481898","url":null,"abstract":"Zero-shot sketch-based image retrieval (ZS-SBIR) aims to utilize freehand sketches for retrieving natural images with similar semantics in realistic zero-shot scenarios. Existing works focus on zero-shot semantic transfer using category word embedding and leveraging teacher-student networks to alleviate catastrophic forgetting of pre-trained models. They aim to retain rich discriminative features to achieve zero-shot semantic transfer. However, the category word embedding method is insufficient in flexibility, thereby limiting their retrieval performances in ZS-SBIR scenarios. In addition, the teacher network used for generating guidance signals results in computational redundancy, requiring repeated processing of mini-batch inputs. To address these issues, we propose a deep residual coupled prompt learning (DRCPL) for ZS-SBIR. Specifically, we leverage the text encoder of CLIP to generate category classification weights, thereby improving the flexibility and generality of zero-shot semantic transfer. To tune text and vision representations effectively, we introduce learnable prompts at the input and freeze the parameters of the CLIP encoder. This approach not only effectively prevents catastrophic forgetting, but also significantly reduces the computational complexity of the model. We also introduce the text-vision prompt coupling function to enhance the coordinated consistency between the text and vision representations, ensuring that the two branches can train collaboratively. Finally, we gradually establish stage feature relationships by learning prompts independently at different early stages to facilitate rich contextual learning. Comprehensive experimental results demonstrate that our DRCPL method achieves state-of-the-art performance in ZS-SBIR tasks.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1493-1507"},"PeriodicalIF":7.5,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliable Data Augmented Contrastive Learning for Sequential Recommendation","authors":"Mankun Zhao;Aitong Sun;Jian Yu;Xuewei Li;Dongxiao He;Ruiguo Yu;Mei Yu","doi":"10.1109/TBDATA.2024.3453752","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3453752","url":null,"abstract":"Sequential recommendation aims to capture users’ dynamic preferences. Due to the limited information in the sequence and the uncertain user behavior, data sparsity has always been a key problem. Although data augmentation methods can alleviate this issue, unreliable data can affect the performance of such models. To solve the above problems, we propose a new framework, namely \u0000<bold>R</b>\u0000eliable \u0000<bold>D</b>\u0000ata Augmented \u0000<bold>C</b>\u0000ontrastive Learning \u0000<bold>Rec</b>\u0000ommender (RDCRec). Specifically, in order to generate more high-quality reliable items for data augmentation, we design a multi-attributes oriented sequence generator. It moves auxiliary information from the input layer to the attention layer for learning a better attention distribution. Then, we replace a percentage of items in the original sequence with reliable items generated by the generator as the augmented sequence, for creating a high-quality view for contrastive learning. In this way, RDCRec can extract more meaningful user patterns by using the self-supervised signals of the reliable items, thereby improving recommendation performance. Finally, we train a discriminator to identify unreplaced items in the augmented sequence thus we can update item embeddings selectively in order to increase the exposure of more reliable items and improve the accuracy of recommendation results. The discriminator, as an auxiliary model, is jointly trained with the generative task and the contrastive learning task. Large experiments on four popular datasets that are commonly used demonstrate the effectiveness of our new method for sequential recommendation.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"10 6","pages":"694-705"},"PeriodicalIF":7.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142600248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distilling Fair Representations From Fair Teachers","authors":"Huan Tian;Bo Liu;Tianqing Zhu;Wanlei Zhou;Philip S. Yu","doi":"10.1109/TBDATA.2024.3460532","DOIUrl":"https://doi.org/10.1109/TBDATA.2024.3460532","url":null,"abstract":"As an increasing number of data-driven deep learning models are deployed in our daily lives, the issue of algorithmic fairness has become a major concern. These models are trained on data that inevitably contains various biases, leading them to learn unfair representations that differ across demographic subgroups, resulting in unfair predictions. Previous work on fairness has attempted to remove subgroup information from learned features, aiming to contribute to similar representations across subgroups and lead to fairer predictions. However, identifying and removing this information is extremely challenging due to the “black box” nature of neural networks. Moreover, removing desired features without affecting other features is difficult, as features are often correlated, potentially harming model prediction performance. This paper aims to learn fair representations without degrading model prediction performance. We adopt knowledge distillation, allowing unfair models to learn fair representations directly from a fair teacher. The proposed method provides a novel approach to obtaining fair representations while maintaining valid prediction performance. We evaluate the proposed method, FairDistill, on four datasets (CIFAR-10, UTKFace, CelebA, and Adult) under diverse settings. Extensive experiments demonstrate the effectiveness and robustness of the proposed method.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1419-1433"},"PeriodicalIF":7.5,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}