IEEE Transactions on Big Data最新文献

Editorial High-Performance Recommender Systems Based on Spatiotemporal Data 基于时空数据的编辑高性能推荐系统

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-07-10 DOI: 10.1109/TBDATA.2024.3451088

Shuo Shang;Qi Liu;Renhe Jiang;Ryosuke Shibasaki;Panos Kalnis;Christian S. Jensen

引用次数: 0

Editorial: Big Data Analytics in Complex Social Information Networks 社论：复杂社会信息网络中的大数据分析

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-07-10 DOI: 10.1109/TBDATA.2024.3485316

Desheng Dash Wu;David L. Olson

引用次数: 0

GE-GNN: Gated Edge-Augmented Graph Neural Network for Fraud Detection 用于欺诈检测的门控边缘增强图神经网络

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-04-18 DOI: 10.1109/TBDATA.2025.3562486

Wenxin Zhang;Cuicui Luo

{"title":"GE-GNN: Gated Edge-Augmented Graph Neural Network for Fraud Detection","authors":"Wenxin Zhang;Cuicui Luo","doi":"10.1109/TBDATA.2025.3562486","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3562486","url":null,"abstract":"Graph Neural Networks (GNNs) play a significant role and have been widely applied in fraud detection tasks, exhibiting substantial improvements in detection performance compared to conventional methodologies. However, within the intricate structure of fraud graphs, fraudsters usually camouflage themselves among a large number of benign entities. An effective solution to address the camouflage problem involves the incorporation of complex and abundant edge information. Nevertheless, existing GNN-based methods frequently neglect to integrate this crucial information into the message passing process, thereby limiting their efficacy. To address the above issues, this study proposes a novel Gated Edge-augmented Graph Neural Network(GE-GNN). Our approach begins with an edge-based feature augmentation mechanism that leverages both node and edge features within a single relation. Subsequently, we apply the augmented representation to the message passing process to update the node embeddings. Furthermore, we design a gate logistic to regulate the expression of augmented information. Finally, we integrate node features across different relations to obtain a comprehensive representation. Extensive experimental results on two real-world datasets demonstrate that the proposed method outperforms several state-of-the-art methods.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1664-1676"},"PeriodicalIF":7.5,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598065","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

Toward High-Quality Spatiotemporal Recommendation: Trajectory Recovery Based on Spatial and Temporal Dependencies 迈向高质量时空推荐：基于时空依赖的轨迹恢复

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-03-14 DOI: 10.1109/TBDATA.2025.3570071

Yihao Zhao;Chenhao Wang;Hongyu Wang;Shunzhi Zhu;Lisi Chen

{"title":"Toward High-Quality Spatiotemporal Recommendation: Trajectory Recovery Based on Spatial and Temporal Dependencies","authors":"Yihao Zhao;Chenhao Wang;Hongyu Wang;Shunzhi Zhu;Lisi Chen","doi":"10.1109/TBDATA.2025.3570071","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3570071","url":null,"abstract":"The rapid advancement of location and information technologies has generated a significant volume of human mobility data, which has been extensively utilized in spatiotemporal recommendation systems, including personalized point-of-interest recommendation, route recommendation, and location-aware event recommendation. Achieving high-quality recommendation results necessitates excellent quality of input trajectory data. However, trajectories obtained from GPS-enabled devices often contain missing and erroneous data that is unevenly distributed over time and highly sparse, which significantly hampers the effectiveness spatiotemporal data analytics. Therefore, trajectory recovery plays an important role in spatiotemporal recommendation systems. The objective of trajectory recovery is to utilize historical trajectories to restore missing locations, providing high-quality data for spatiotemporal recommendation systems. The development of an effective trajectory recovery mechanism faces three major challenges: 1) Complex and multi-granularity transition patterns among different locations; 2) Difficulty in discovering spatio-temporal dependencies; and 3) Data sparsity and noise. To address these challenges, we propose an attentional model with spatio-temporal recurrent neural networks, ARMove, to recover human mobility from long and sparse trajectories. In ARMove, we first design a spatio-temporal weighted recurrent neural network to capture users’ long-term preferences. Next, we introduce a multi-granularity trajectory encoder to model complex transition patterns and multi-level periodicity of human mobility. An attention-based history aggregation module is proposed to leverage historical mobility information. Extensive evaluation results reveal that our model outperforms the state-of-the-art models, demonstrating its ability to reconstruct high-quality and fine-grained human mobility trajectories.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 4","pages":"1628-1639"},"PeriodicalIF":7.5,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597721","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

Editorial Emerging Horizons: The Rise of Large Language Models and Cross-Modal Generative AI 新兴的视野：大语言模型和跨模态生成人工智能的兴起

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-03-14 DOI: 10.1109/TBDATA.2025.3537217

Guang Yang;Jing Zhang;Giorgos Papanastasiou;Ge Wang;Dacheng Tao

引用次数: 0

Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task 探索权衡：统一的大型语言模型与高度特异性放射学NLI任务的局部微调模型

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-02-13 DOI: 10.1109/TBDATA.2025.3536928

Zihao Wu;Lu Zhang;Chao Cao;Xiaowei Yu;Zhengliang Liu;Lin Zhao;Yiwei Li;Haixing Dai;Chong Ma;Gang Li;Wei Liu;Quanzheng Li;Dinggang Shen;Xiang Li;Dajiang Zhu;Tianming Liu

{"title":"Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task","authors":"Zihao Wu;Lu Zhang;Chao Cao;Xiaowei Yu;Zhengliang Liu;Lin Zhao;Yiwei Li;Haixing Dai;Chong Ma;Gang Li;Wei Liu;Quanzheng Li;Dinggang Shen;Xiang Li;Dajiang Zhu;Tianming Liu","doi":"10.1109/TBDATA.2025.3536928","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3536928","url":null,"abstract":"Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capability in various open-domain tasks, their adequacy in highly specific fields like radiology remains untested. Radiology presents unique linguistic phenomena distinct from open-domain data due to its specificity and complexity. Assessing the performance of large language models (LLMs) in such specific domains is crucial not only for a thorough evaluation of their overall performance but also for providing valuable insights into future model design directions: whether model design should be generic or domain-specific. To this end, in this study, we evaluate the performance of ChatGPT/GPT-4 on a radiology natural language inference (NLI) task and compare it to other models fine-tuned specifically on task-related data samples. We also conduct a comprehensive investigation on ChatGPT/GPT-4’s reasoning ability by introducing varying levels of inference difficulty. Our results show that 1) ChatGPT and GPT-4 outperform other LLMs in the radiology NLI task and 2) other specifically fine-tuned Bert-based models require significant amounts of data samples to achieve comparable performance to ChatGPT/GPT-4. These findings not only demonstrate the feasibility and promise of constructing a generic model capable of addressing various tasks across different domains, but also highlight several key factors crucial for developing a unified model, particularly in a medical context, paving the way for future artificial general intelligence (AGI) systems. We release our code and data to the research community.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"1027-1041"},"PeriodicalIF":7.5,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949276","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

Large Language Model-Informed ECG Dual Attention Network for Heart Failure Risk Prediction 基于大语言模型的心电双注意网络心衰风险预测

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-30 DOI: 10.1109/TBDATA.2025.3536922

Chen Chen;Lei Li;Marcel Beetz;Abhirup Banerjee;Ramneek Gupta;Vicente Grau

{"title":"Large Language Model-Informed ECG Dual Attention Network for Heart Failure Risk Prediction","authors":"Chen Chen;Lei Li;Marcel Beetz;Abhirup Banerjee;Ramneek Gupta;Vicente Grau","doi":"10.1109/TBDATA.2025.3536922","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3536922","url":null,"abstract":"Heart failure (HF) poses a significant public health challenge, with a rising global mortality rate. Early detection and prevention of HF could significantly reduce its impact. We introduce a novel methodology for predicting HF risk using 12-lead electrocardiograms (ECGs). We present a novel, lightweight dual attention ECG network designed to capture complex ECG features essential for early HF risk prediction, despite the notable imbalance between low and high-risk groups. This network incorporates a cross-lead attention module and 12 lead-specific temporal attention modules, focusing on cross-lead interactions and each lead's local dynamics. To further alleviate model overfitting, we leverage a large language model (LLM) with a public ECG-Report dataset for pretraining on an ECG-Report alignment task. The network is then fine-tuned for HF risk prediction using two specific cohorts from the U.K. Biobank study, focusing on patients with hypertension (UKB-HYP) and those who have had a myocardial infarction (UKB-MI). The results reveal that LLM-informed pre-training substantially enhances HF risk prediction in these cohorts. The dual attention design not only improves interpretability but also predictive accuracy, outperforming existing competitive methods with C-index scores of 0.6349 for UKB-HYP and 0.5805 for UKB-MI. This demonstrates our method's potential in advancing HF risk assessment with clinical complex ECG data.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"948-960"},"PeriodicalIF":7.5,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858425","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Use of Transfer Learning for Affordable In-Context Fake Review Generation 使用迁移学习在可承受的情境中生成虚假评论

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-30 DOI: 10.1109/TBDATA.2025.3536927

Luis Ibañez-Lissen;Lorena González-Manzano;José M. de Fuentes;Manuel Goyanes

引用次数: 0

Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions 层次多关系图表示学习用于药物-药物相互作用的大规模预测

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-30 DOI: 10.1109/TBDATA.2025.3536924

Mengying Jiang;Guizhong Liu;Yuanchao Su;Weiqiang Jin;Biao Zhao

{"title":"Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug Interactions","authors":"Mengying Jiang;Guizhong Liu;Yuanchao Su;Weiqiang Jin;Biao Zhao","doi":"10.1109/TBDATA.2025.3536924","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3536924","url":null,"abstract":"Most existing methods for predicting drug-drug interactions (DDI) predominantly concentrate on capturing the explicit relationships among drugs, overlooking the valuable implicit correlations present between drug pairs (DPs), which leads to weak predictions. To address this issue, this paper introduces a hierarchical multi-relational graph representation learning (HMGRL) approach. Within the framework of HMGRL, we leverage a wealth of drug-related heterogeneous data sources to construct heterogeneous graphs, where nodes represent drugs and edges denote clear and various associations. The relational graph convolutional network (RGCN) is employed to capture diverse explicit relationships between drugs from these heterogeneous graphs. Additionally, a multi-view differentiable spectral clustering (MVDSC) module is developed to capture multiple valuable implicit correlations between DPs. Within the MVDSC, we utilize multiple DP features to construct graphs, where nodes represent DPs and edges denote different implicit correlations. Subsequently, multiple DP representations are generated through graph cutting, each emphasizing distinct implicit correlations. The graph-cutting strategy enables our HMGRL to identify strongly connected communities of graphs, thereby reducing the fusion of irrelevant features. By combining every representation view of a DP, we create high-level DP representations for predicting DDIs. Two genuine datasets spanning three distinct tasks are adopted to gauge the efficacy of our HMGRL. Experimental outcomes unequivocally indicate that HMGRL surpasses several leading-edge methods in performance.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"961-975"},"PeriodicalIF":7.5,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949322","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

TinyLVLM-eHub: Towards Comprehensive and Efficient Evaluation for Large Vision-Language Models TinyLVLM-eHub：面向大型视觉语言模型的综合高效评估

IF 7.5 3区计算机科学

IEEE Transactions on Big Data Pub Date : 2025-01-30 DOI: 10.1109/TBDATA.2025.3536930

Wenqi Shao;Meng Lei;Yutao Hu;Peng Gao;Peng Xu;Kaipeng Zhang;Fanqing Meng;Siyuan Huang;Hongsheng Li;Yu Qiao;Ping Luo

{"title":"TinyLVLM-eHub: Towards Comprehensive and Efficient Evaluation for Large Vision-Language Models","authors":"Wenqi Shao;Meng Lei;Yutao Hu;Peng Gao;Peng Xu;Kaipeng Zhang;Fanqing Meng;Siyuan Huang;Hongsheng Li;Yu Qiao;Ping Luo","doi":"10.1109/TBDATA.2025.3536930","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3536930","url":null,"abstract":"Large Vision-Language Models (LVLMs) have made significant strides in various multimodal tasks. Notably, GPT4V, Claude, Gemini, and others showcase exceptional multimodal capabilities, marked by profound comprehension and reasoning skills. This study introduces a comprehensive and efficient evaluation framework, TinyLVLM-eHub, to assess LVLMs’ performance, including proprietary models. TinyLVLM-eHub covers six key multimodal capabilities, such as visual perception, knowledge acquisition, reasoning, commonsense understanding, object hallucination, and embodied intelligence. The benchmark, utilizing 2.1K image-text pairs, provides a user-friendly and accessible platform for LVLM evaluation. The evaluation employs the ChatGPT Ensemble Evaluation (CEE) method, which improves alignment with human evaluation compared to word-matching approaches. Results reveal that closed-source API models like GPT4V and GeminiPro-V excel in most capabilities compared to previous open-source LVLMs, though they show some vulnerability in object hallucination. This evaluation underscores areas for LVLM improvement in real-world applications and serves as a foundational assessment for future multimodal advancements.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 3","pages":"933-947"},"PeriodicalIF":7.5,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949200","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