IEEE Transactions on Big Data最新文献_第2页

A Malicious and Anti-Malicious Information Propagation Dynamics Model Based on Higher-Order Diffusion Networks 基于高阶扩散网络的恶意与反恶意信息传播动力学模型

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2026-06-01 Epub Date: 2026-02-27 DOI: 10.1109/TBDATA.2026.3668545

Tun Li;Chengkai Liu;Jianfeng Liu;Rong Wang;Hongjun Zhu;Yunpeng Xiao

{"title":"A Malicious and Anti-Malicious Information Propagation Dynamics Model Based on Higher-Order Diffusion Networks","authors":"Tun Li;Chengkai Liu;Jianfeng Liu;Rong Wang;Hongjun Zhu;Yunpeng Xiao","doi":"10.1109/TBDATA.2026.3668545","DOIUrl":"https://doi.org/10.1109/TBDATA.2026.3668545","url":null,"abstract":"As people benefit from the convenience of social networks, they are also exposed to security risks from malicious information. This article proposes a malicious and anti-malicious information propagation dynamics model based on higher-order diffusion networks to address these issues. Firstly, to tackle the problem of individuals’ behavior being swayed by the emotional content of malicious information during propagation, we have established an emotional influence mechanism. At the same time, adding user preferences and external drivers proposes a way to calculate individual influence. Secondly, in response to the discrepancies in the propagation efficiency of malicious information among varying users, we establish a higher-order diffusion network and quantify these differences through the stratification of users into three distinct layers of nodes. Additionally, considering the implicit relationship among potential user groups, we establish a group interaction mechanism by mining group attributes and refine the structure of the propagation network. Lastly, given the coexistence and opposition between malicious and anti-malicious information, dynamic game theory is applied to define a state transition equation incorporating anti-malicious information propagators. Consequently, we propose the SAIR model, a novel paradigm for understanding the propagation of malicious and anti-malicious information using higher-order diffusion networks.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"12 3","pages":"1020-1032"},"PeriodicalIF":8.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507443","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}

引用次数: 0

RAT: Residual Attention Transformer for Tabular Data 表格数据的剩余注意转换器

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2026-06-01 Epub Date: 2025-12-31 DOI: 10.1109/TBDATA.2025.3650053

Aiwen Wang;Xinglin Piao;Xinze Zhang;Yujia Guo;Yong Zhang

{"title":"RAT: Residual Attention Transformer for Tabular Data","authors":"Aiwen Wang;Xinglin Piao;Xinze Zhang;Yujia Guo;Yong Zhang","doi":"10.1109/TBDATA.2025.3650053","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3650053","url":null,"abstract":"The effectiveness of Transformer-based methods in tabular data processing has been extensively validated. However, most existing models primarily adopt Transformer-based encoder architectures, which overly emphasize feature correlations while neglecting the unique explicit information representation characteristics of tabular data. The significantly higher information density of tabular data compared to textual data introduces noise that degrades performance. Consequently, directly applying language models to tabular data often yields suboptimal results. To address this limitation, we propose the Residual Attention Transformer (RAT), a Transformer-based mechanism specifically designed for tabular data. In subsequent sections, the Residual Attention Transformer will be referred to as RAT. RAT introduces residual connections into the self-attention mechanism, effectively preserving raw information while capturing dependencies with higher-order features, thus enhancing the richness of information representation. Additionally, we design a simplified Transformer output head, termed Light-Transformer, which consists of a lightweight Transformer block specifically dedicated to the output stage. This design not only significantly reduces the model’s parameter count, but also greatly improves training and inference efficiency. Extensive experiments were conducted on six public datasets to evaluate our model. The results show that the RAT model consistently outperforms other models in various tasks, confirming its effectiveness and superiority.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"12 3","pages":"909-920"},"PeriodicalIF":8.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506444","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}

引用次数: 0

Near-Optimal Joint Compression-Encryption Schemes for Big Data Storage With Asymmetric Numeral Systems 非对称数字系统下大数据存储的近最优联合压缩加密方案

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2026-06-01 Epub Date: 2025-12-04 DOI: 10.1109/TBDATA.2025.3639931

Na Wang;Xiaolong Hong;Mingyin Li;Wei Yan;Shuo Shao;Chuan Qin;Ching-Chun Chang;Chin-Chen Chang

{"title":"Near-Optimal Joint Compression-Encryption Schemes for Big Data Storage With Asymmetric Numeral Systems","authors":"Na Wang;Xiaolong Hong;Mingyin Li;Wei Yan;Shuo Shao;Chuan Qin;Ching-Chun Chang;Chin-Chen Chang","doi":"10.1109/TBDATA.2025.3639931","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3639931","url":null,"abstract":"Asymmetric numeral systems (ANS) is a widely used entropy coding method in commercial compressors due to its high performance. Joint compression and encryption techniques can offer reliability and cost-effectiveness for secure Big Data storage. However, existing joint compression-encryption schemes for ANS coding often suffer from either increased storage space requirements or limited security. To address these issues, this paper proposes two ANS-based joint compression-encryption algorithms that provide considerable security with almost no compression loss. The first scheme, based on interval swapping, employs a cryptographically secure ChaCha20 generator to perturb the order of contiguous intervals, thereby introducing controlled randomness into the encoding process. The second scheme, based on interval splitting, discards the conventional assumption of representing each symbol with a single contiguous interval, instead assigning multiple sub-intervals to enhance both security and flexibility. In addition, a sequence of output permutations is applied to further strengthen resistance against attacks. Experimental results show that the proposed methods reduce compression loss by approximately 3.83% compared with existing schemes, while the interval swapping scheme achieves a 46.9% reduction in time cost. Security analysis confirms that the enlarged key space significantly increases robustness against brute-force attacks. These results demonstrate that the proposed approaches effectively balance compression efficiency and encryption strength, offering a lightweight and secure solution for Big Data storage.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"12 3","pages":"768-786"},"PeriodicalIF":8.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506436","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}

引用次数: 0

PLM$^{3}$RC: A PLM-Based Method for Multilingual Relation Classification Without Using Parallel Corpora 基于PLM的不使用平行语料库的多语言关系分类方法

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2026-06-01 Epub Date: 2025-12-15 DOI: 10.1109/TBDATA.2025.3644648

Jiapin Lu;Desheng Wu

{"title":"PLM$^{3}$RC: A PLM-Based Method for Multilingual Relation Classification Without Using Parallel Corpora","authors":"Jiapin Lu;Desheng Wu","doi":"10.1109/TBDATA.2025.3644648","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3644648","url":null,"abstract":"When building knowledge bases using relation classification (RC) techniques, it is essential to handle data from various languages and domains. However, most existing multilingual RC methods require parallel corpora and some may lead to obviously increased computation overhead. To address these challenges, we propose a multilingual RC method based on PLMs, called PLM<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>RC. Our method first introduce entity prompts and multilingual PLMs to construct a shared encoder network, and capture shared language-invariant knowledge of relation classification. Then multiple domain-specific classifiers are introduced to capture language- and domain-specific knowledge. For realizing multilingual knowledge transfer, we further introduce adversarial learning and design linear discriminators. We conducted a series of experiments to verify the effectiveness of our method. Our experiments show that PLM<inline-formula><tex-math>$^{3}$</tex-math></inline-formula>RC outperform other mainstream RC method in multilingual scenarios, with regular computing resources. Besides, the results clearly demonstrate that our method can effectively leverages multilingual data for knowledge transfer, without using parallel corpora.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"12 3","pages":"787-800"},"PeriodicalIF":8.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506677","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}

引用次数: 0

A Comprehensive Survey of Knowledge Graph Reasoning: Approaches and Applications 知识图推理的综合综述：方法和应用

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2026-06-01 Epub Date: 2026-02-27 DOI: 10.1109/TBDATA.2026.3668633

Guanglin Niu;Bo Li;Yangguang Lin

{"title":"A Comprehensive Survey of Knowledge Graph Reasoning: Approaches and Applications","authors":"Guanglin Niu;Bo Li;Yangguang Lin","doi":"10.1109/TBDATA.2026.3668633","DOIUrl":"https://doi.org/10.1109/TBDATA.2026.3668633","url":null,"abstract":"Knowledge graph reasoning (KGR) aims to infer novel knowledge based on existing facts in knowledge graphs (KGs), playing a crucial role in various cognition intelligence systems across diverse domains. The previous review works explore KGR models from specific perspectives such as KG types and embedding spaces. In contrast, this survey provides a more comprehensive perspective of KGR from foundational approaches and their applications. Notably, some seldom-attended approaches such as negative sampling strategies, popular open-source libraries, and rule-guided KGR paradigms are carefully reviewed. Besides, we explore advanced techniques, such as large language models (LLMs) and their impact on KGR. The comparison among foundational models are analyzed to declare their strengths and limitations. More interestingly, this is the first effort to provide a taxonomy of real-world KGR applications for both horizontal and vertical domains. Furthermore, we highlight the challenges and opportunities in the field of KGR, including trustworthiness, multimodal reasoning, continual learning, uncertainty and LLM-driven approaches. This work aims to bridge the gap between theoretical advancements and practical deployment of KGR models, and outline promising future directions.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"12 3","pages":"721-740"},"PeriodicalIF":8.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507176","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}

引用次数: 0

Contrastive Prompt Learning in Structured Graph Networks for Multimodal Fake News Detection 结构图网络中多模态假新闻检测的对比提示学习

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2026-06-01 Epub Date: 2026-02-27 DOI: 10.1109/TBDATA.2026.3668483

Ying Guo;Kexin Zhen;Jie Liu

{"title":"Contrastive Prompt Learning in Structured Graph Networks for Multimodal Fake News Detection","authors":"Ying Guo;Kexin Zhen;Jie Liu","doi":"10.1109/TBDATA.2026.3668483","DOIUrl":"https://doi.org/10.1109/TBDATA.2026.3668483","url":null,"abstract":"With social media now the primary channel for information dissemination, multimodal disinformation poses a severe challenge to cyberspace governance and public safety due to its heightened deceptiveness and viral potential. Current detection methods predominantly rely on simple feature concatenation or shallow interaction models, which struggle to capture semantic relationships and inconsistencies across modalities. Moreover, they also universally face the semantic gap issue caused by misalignment between pre-training tasks and downstream classification objectives. To address these issues, this paper proposes a Contrastive Prompt Learning in Structured Graph Networks (CPLS) model. CPLS comprises three core modules: 1) Structured graph network construction module that builds graph structures preserving key semantics through semantic-aware graph pruning and fusion strategies; 2) Structured graph network optimization module that uses data augmentation and contrastive learning to capture robust local node features and global graph structures; 3) Contrastive prompt learning module that innovatively integrates prompt learning with graph contrastive learning, reframing downstream classification tasks as contrastive tasks to unify optimization of pre-training and downstream objectives. Experiments on public datasets show that our method achieves accuracy improvements of 2.4% on Weibo and 4.5% on Twitter.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"12 3","pages":"1045-1057"},"PeriodicalIF":8.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148507429","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}

引用次数: 0

Benchmarking LLMs Against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels 对人类翻译的基准法学硕士：跨语言，领域和专业水平的综合评估

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2026-06-01 Epub Date: 2025-12-15 DOI: 10.1109/TBDATA.2025.3644594

Jianhao Yan;Pingchuan Yan;Yulong Chen;Jing Li;Xianchao Zhu;Yue Zhang

{"title":"Benchmarking LLMs Against Human Translators: A Comprehensive Evaluation Across Languages, Domains, and Expertise Levels","authors":"Jianhao Yan;Pingchuan Yan;Yulong Chen;Jing Li;Xianchao Zhu;Yue Zhang","doi":"10.1109/TBDATA.2025.3644594","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3644594","url":null,"abstract":"This study presents a comprehensive evaluation of the translation capabilities of existing LLMs, such as GPT-4, ALMA-R, and Deepseek-R1, compared to human translators of varying expertise levels. Through systematic human evaluation using the MQM schema, we assess translations across three language pairs (Chinese <inline-formula><tex-math>$longleftrightarrow$</tex-math></inline-formula> English, Russian <inline-formula><tex-math>$longleftrightarrow$</tex-math></inline-formula> English, and Chinese <inline-formula><tex-math>$longleftrightarrow$</tex-math></inline-formula> Hindi) and three domains (News, Technology, and Biomedical). Our findings reveal that LLMs achieve performance comparable to junior-level translators in terms of total errors, while still lagging behind senior translators. Unlike traditional Neural Machine Translation systems, which show significant performance degradation in resource-poor language directions, LLMs like GPT-4 maintain consistent translation quality across all evaluated language pairs. Through qualitative analysis, we identify distinctive patterns in translation approaches: GPT-4 tends toward overly literal translations and exhibits lexical inconsistency, while human translators sometimes over-interpret context and introduce hallucinations. This study presents a systematic comparison between LLMs and human translators across different proficiency levels, providing valuable insights into the current capabilities and limitations of LLM-based translation systems.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"12 3","pages":"801-813"},"PeriodicalIF":8.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508430","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}

引用次数: 0

PieSketch: Finding Core Composition of Frequent Keys in High-Speed Data Streams PieSketch：在高速数据流中寻找频繁键的核心组成

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2026-06-01 Epub Date: 2025-12-15 DOI: 10.1109/TBDATA.2025.3644650

Yuheng Zhou;Guoju Gao;He Huang;Yu-E Sun;Yang Du

{"title":"PieSketch: Finding Core Composition of Frequent Keys in High-Speed Data Streams","authors":"Yuheng Zhou;Guoju Gao;He Huang;Yu-E Sun;Yang Du","doi":"10.1109/TBDATA.2025.3644650","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3644650","url":null,"abstract":"Key-value data streams are widely spread in modern information systems. This paper delves into the inner features of individual keys and introduces a new measurement task called <bold>Core Composition Estimation for Frequent Keys (CCEFK)</b>. CCEFK aims to identify the most significant values associated with an item’s key, which can be fully applied to a wide range of real-world scenarios. To address this quite challenging, multidimensional CCEFK mission, we propose PieSketch, a novel sketch-based algorithm that supports three types of queries related to CCEFK. PieSketch employs a three-level data structure and a two-staged insertion algorithm to process the keys and values of data streams separately and sequentially, achieving fine-grained data summarization for both. Through some rigorous theoretical analysis, we prove that PieSketch can achieve a good performance guarantee with low time and space complexity. We further present three optimizations to enhance PieSketch comprehensively: Sector Morphing addresses the diversity in value distribution among different keys with a dynamically adjusting strategy, significantly improving memory efficiency; Early Filtration ensures PieSketch’s stability in extreme conditions; SIMD Acceleration speeds up PieSketch for data processing. We finally conducted extensive experiments on four real-world datasets to evaluate PieSketch’s excellent performance. The results show that PieSketch significantly outperforms state-of-the-art techniques, achieving 23.67×, 88.41×, and 15.80× better accuracy on three types of queries, respectively, and demonstrating 1.84 times higher insertion throughput on average.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"12 3","pages":"829-842"},"PeriodicalIF":8.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148505814","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}

引用次数: 0

Modeling Complex Infectious Disease Dynamics by Distinguishing Susceptible Populations 通过区分易感人群建立复杂传染病动力学模型

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2026-06-01 Epub Date: 2026-01-13 DOI: 10.1109/TBDATA.2026.3653627

Siyang Xie;Zhili Zhao;Nana Zhang;Ahui Hu;Li Wan;Ruiyi Yan

{"title":"Modeling Complex Infectious Disease Dynamics by Distinguishing Susceptible Populations","authors":"Siyang Xie;Zhili Zhao;Nana Zhang;Ahui Hu;Li Wan;Ruiyi Yan","doi":"10.1109/TBDATA.2026.3653627","DOIUrl":"https://doi.org/10.1109/TBDATA.2026.3653627","url":null,"abstract":"Predicting the dynamics of infectious diseases plays an important role in their prevention and control. However, traditional epidemic models, such as susceptible–infected–recovered (SIR), susceptible–exposed–infected–recovered (SEIR), and their variants, are often inadequate for accurately capturing behavior-driven and heterogeneous transmission at scale. In this study, we propose a nonlinear dynamic model—the susceptible–low-susceptible–exposed–infected–recovered–death (SLEIRD) model—that further extends the SEIR framework. SLEIRD explicitly differentiates susceptible populations by introducing a low-susceptible group adopting protective measures, models four infection channels from susceptible/low-susceptible to exposed/infectious groups, thereby providing a behavior-aware and structurally richer compartmentalization than classical models. To validate its effectiveness, we compare SLEIRD with other classical epidemic models using real-world epidemic data, predicting confirmed cases and deaths over five, ten, fifteen, twenty, and thirty days. Experimental results show that SLEIRD outperforms SIR, improving the root mean squared error (RMSE) for daily confirmed cases and deaths by 6.0% –46.3% and 31.1% –62.3%, respectively; compared with SEIR, SLEIRD reduces the average RMSE for daily confirmed cases and deaths by 6.8% –56.6% and 59.8% –79.6%, respectively. These findings indicate that SLEIRD more accurately simulates complex spread dynamics and offers a scalable, behavior-aware tool for data-driven epidemic forecasting with practical value for disease control and prevention.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"12 3","pages":"948-961"},"PeriodicalIF":8.1,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148506446","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}

引用次数: 0

Abstractive Summarization of Large Document Collections Using GPT 使用GPT的大型文档集合的抽象摘要

IF 8.1 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2026-06-01 Epub Date: 2026-02-26 DOI: 10.1109/TBDATA.2026.3668604

Shengjie Liu;Christopher G. Healey

引用次数: 0