IEEE Transactions on Big Data最新文献

{"title":"2025 Reviewers List*","authors":"","doi":"10.1109/TBDATA.2026.3652336","DOIUrl":"https://doi.org/10.1109/TBDATA.2026.3652336","url":null,"abstract":"","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"12 1","pages":"301-306"},"PeriodicalIF":5.7,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11357242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982289","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}

{"title":"Dual-Channel Learning Framework for miRNA-Drug Interaction Prediction Based on Structural Features and Signed Bipartite Graph Neural Network","authors":"Xiaoxuan Zhang;Xiujuan Lei;Ling Guo;Ming Chen;Fang-Xiang Wu;Yi Pan","doi":"10.1109/TBDATA.2025.3639954","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3639954","url":null,"abstract":"MicroRNAs (miRNAs) play a vital role in regulating a wide range of biological functions and are key players in the development of many complex human diseases, making them novel therapeutic targets for drug development. Given the high expenses and time demands of traditional experimental methods, it is essential to develop efficient computational approaches for predicting miRNA-drug interactions (MDIs). This article presents a dual-channel learning framework, SSMDI, based on structural features and Signed Bipartite Graph Neural Network (SBGNN) for predicting MDIs. Firstly, Graph Isomorphism Networks (GIN) is employed to extract molecular graph features of drugs. Meanwhile, a combined framework of Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM) network and Self-attention Mechanism is utilized to capture sequence features of miRNAs. Compared with traditional networks, signed networks can deliver richer semantic information in drugs and miRNAs. Therefore, SBGNN is then used to aggregate and update the signed topological features of miRNAs and drugs. Finally, structural and signed topological features are integrated to predict MDIs. The predictive performance of the model is evaluated using 5-fold cross-validation (CV), achieving AUC of 0.9447 and AUPR of 0.9238. The case study further demonstrates the effectiveness of SSMDI in predicting MDIs. In summary, the SSMDI model proves to be an accurate tool for predicting MDIs, which holds significant implications for drug development and miRNA-based therapeutic research.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"12 2","pages":"688-701"},"PeriodicalIF":5.7,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440665","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":"Lafa: Unlocking Superior Memory Efficiency via Adaptive Metadata Strategy for Scalable Large-Scale Dataset Loading","authors":"Cong Wang;Yang Luo;Ke Wang;Hui Zhang;Naijie Gu;Ran Zhang;Wenzhuo Du;Fan Yu;Jun Yu","doi":"10.1109/TBDATA.2025.3640011","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3640011","url":null,"abstract":"The rapid growth of deep learning models and the increasing demand for large-scale datasets have posed unprecedented challenges for data loading and memory management. Existing frameworks (e.g., PyTorch, TensorFlow) often encounter performance bottlenecks when handling large datasets resulting in inefficiencies and excessive memory usage. To address these issues, we propose Lafa, a dynamic metadata loading mechanism optimized for efficient large-scale dataset processing. Lafa introduces the. Lafa format and an adaptive loading strategy with three modes to balance memory usage and loading performance, along with a local shuffle approach that reduces memory overhead and computational complexity while preserving data randomness. Experimental results on GPU (RTX 3090) and Ascend (910 A) platforms demonstrate that Lafa significantly improves memory efficiency compared to existing frameworks. Specifically, for every 10 million samples loaded, Lafa reduces additional memory consumption by a factor of 1.33× to 31.34× across various dataset types, relative to the most memory-efficient baseline among PyTorch, TensorFlow, and MindSpore.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"12 2","pages":"674-687"},"PeriodicalIF":5.7,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440667","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 Fast Linearithmic Graph Clustering Approach for Big Data Using Gravitational Attraction Principle","authors":"Mohammad Maksood Akhter;Abdul Atif Khan;Rashmi Maheshwari;Sraban Kumar Mohanty","doi":"10.1109/TBDATA.2025.3639917","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3639917","url":null,"abstract":"With the exponential growth of Big Data in domains such as healthcare, genomics, and sensor networks, computationally efficient and effective clustering techniques have become essential for uncovering meaningful patterns. Traditional clustering methods face fundamental limitations in Big Data analysis. K-means is among the fastest known approaches, but it fails to capture non-spherical clusters. Hierarchical clustering can detect arbitrary shapes but suffers from sub-cubic complexity, while many state-of-the-art methods still incur quadratic complexity. Moreover, most existing approaches fail to capture the intrinsic structure of data. In this context, graph-based clustering has emerged as a powerful alternative due to its ability to model geometric relationships and reveal underlying structures. However, existing graph-based techniques typically incur quadratic complexity, limiting their scalability. The objective of this work is to develop a scalable graph-based clustering framework that reduces complexity while preserving clustering quality in large, noisy, and high-dimensional datasets. To achieve this, we propose a fast graph clustering framework with overall complexity <inline-formula><tex-math>$mathcal {O}(N lg N)$</tex-math></inline-formula>, where <inline-formula><tex-math>$N$</tex-math></inline-formula> denotes the number of data points. The method employs a two-stage dispersion-based partitioning to generate cohesive sub-clusters, followed by the construction of a sparse graph on sub-cluster centers to efficiently capture adjacency. Sub-clusters are then merged iteratively using a gravitational-force-inspired attraction model, enabling the discovery of coherent structures with reduced computation. Extensive experiments on 41 multi-scale datasets demonstrate that our method consistently outperforms traditional and state-of-the-art approaches, achieving average 27.33% higher clustering accuracy while reducing runtime by more than 86.64% on average. These results highlight both the innovation and the effectiveness of the proposed approach, making it highly suitable for Big Data analytics.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"12 2","pages":"661-673"},"PeriodicalIF":5.7,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147440666","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":"Graph-Based Contract Sensing Framework for Smart Contract Vulnerability Detection","authors":"Yan Pang;Xiangfu Liu;Teng Huang;Yile Hong;Jiahui Huang;Sisi Duan;Changyu Dong","doi":"10.1109/TBDATA.2025.3594303","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3594303","url":null,"abstract":"Smart contract vulnerabilities have led to significant economic losses, threatening blockchain security and development. Graph neural network (GNN)-based approaches, which capture the structural properties of contracts and leverage code dependencies to better understand contract behavior, have become widely used for vulnerability detection. However, these approaches face challenges in losing valuable information during graph construction and failing to capture rich semantic content, while traditional GNNs struggle with long-range dependencies and global context in complex contract graphs. To address these challenges, we propose ConSense, a GNN-based Contract Sensing Framework for Smart Contract Vulnerability Detection. ConSense comprises two core components: the smart contract graph generator, which constructs contract graphs while retaining both structural and semantic information, and ExploreFormer, which effectively integrates local and global context using advanced attention mechanisms for vulnerability detection. Comprehensive experimental evaluations were performed on the IR-ESCD and SCVHunter-SCD datasets. For instance, the IR-ESCD benchmark—which encompasses eight distinct vulnerability categories—demonstrates that ConSense attains an average detection accuracy of 97.74%, with a mean processing time of 0.648 seconds per contract. These results signify a statistically significant improvement over state-of-the-art methods in both precision and computational efficiency.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3356-3368"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11104930","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479600","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}

{"title":"Vertical Federated Representation Synthesis for Non-Aligned Samples in the Active Party","authors":"Jintao Liang;Sen Su;Zhenya Wang","doi":"10.1109/TBDATA.2025.3566595","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3566595","url":null,"abstract":"In vertical federated learning (VFL) settings, all parties obtain aligned samples of common users identified through private set intersection. For these aligned samples, each party holds disjoint features, while only the active party possesses the label information. As the initiator of model training, the active party can solely benefit from limited aligned samples, overlooking the valuable information contained in its non-aligned local samples. To address the constraint of failing to incorporate the active party's non-aligned samples in practical VFL, this paper presents a <bold>V</b>ertical <bold>Fed</b>erated <bold>R</b>epresentation <bold>S</b>ynthesis (VFedRS) approach for the active party. Its main idea is to transfer the knowledge of common samples to the active party's local feature space, thereby enhancing the performance of the task model trained locally within the active party. Specifically, we first propose a vertical federated principal component analysis module to extract accurate federated representations for common samples under privacy guarantees. To transfer knowledge from federated representations, we then build an input-conditional generative model to synthesize non-aligned samples’ representations for the active party. Subsequently, the active party can leverage data representations to locally train any type of machine learning model suited to the downstream task. We conduct extensive experiments on two tabular datasets and two image datasets in the vertically partitioned setting, and the evaluation results validate the effectiveness of our approach.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"2914-2928"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479604","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":"Efficient FCTN Decomposition With Structural Sparsity for Noisy Tensor Completion","authors":"Wei-Jian Huang;Li Huang;Tai-Xiang Jiang;Yu-Bang Zheng;Guisong Liu","doi":"10.1109/TBDATA.2025.3588029","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3588029","url":null,"abstract":"Recently, the fully-connected tensor network (FCTN) decomposition has shown a powerful capability of depicting intrinsic correlations between any pair of tensor modes. But there exists a challenging question in FCTN decomposition-based methods, i.e., the accurate determination of the complicated FCTN-rank, which contains <inline-formula><tex-math>${N(N-1)}/{2}$</tex-math></inline-formula> elements for <inline-formula><tex-math>$N$</tex-math></inline-formula>th-order tensors. In this paper, we design a structural sparsity regularization for the FCTN decomposition, which estimates the complicated FCTN-rank by adaptively pruning near-zero groups in FCTN factor. Based on this regularization, we propose a noisy tensor completion (NTC) model, aiming at the recovery of a tensor from its partial and noisy observation. Besides, we design a proximal alternating minimization (PAM)-based algorithm to solve the model. In theorem, we prove a guarantee for the global convergence of the developed algorithm. To further accelerate our method for large-scale data sets, we customize the randomized block sampling strategy for general tensor network decomposition methods by updating factors from small samples. Experiments demonstrate that our strategy can accurately estimate the FCTN-rank and achieve better reconstruction performances, and our methods outperform the state-of-the-art methods in the reconstruction of different types of real-world tensors.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3225-3239"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148481401","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":"Bi-Directional Spatiotemporal Gated Graph Convolutional Network for Traffic Speed Prediction","authors":"Chaolong Jia;Fu Jiang;Bingyu Huang;Zheyi Kang;Rong Wang;Yunpeng Xiao","doi":"10.1109/TBDATA.2025.3576009","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3576009","url":null,"abstract":"Accurate traffic speed prediction plays a key role in transportation planning. Inspired by advanced natural language processing (NLP) techniques in speech recognition, this paper proposes a bidirectional spatio-temporal gated graph convolutional network (BiSTGG) based on the attention mechanism for urban traffic speed prediction. The model analogizes the average daily traffic speed with word frequencies in speech, thus establishing a novel semantic perspective for traffic modeling. In order to comprehensively assess the similarity between traffic speed trajectories, the model introduces a dynamic temporal regularization (DTW) algorithm, which enables the construction of dynamic semantic similarity subgraphs that go beyond the traditional spatial proximity modeling approach. Meanwhile, the model applies the multi-head graph attention mechanism to ensure that the complex and diverse interactions in the road network can be captured effectively. In addition, the model introduces spatial identification vectors to enhance robustness and direct attention to focus on key features. The model also incorporates a bi-directional gated recurrent unit (Bi-GRU) structure with an attention mechanism to capture the complex forward and backward time dependencies in traffic patterns. Experiments on two large-scale real datasets show that BiSTGG performs well in terms of prediction accuracy and robustness, and significantly improves real-time traffic speed prediction.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3090-3102"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148478886","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 Unified Hypergraph Framework for Inter and Intra-Session Dynamics in Session-Based Social Recommendations","authors":"Bilal Khan;Jia Wu;Jian Yang;Malik Khizar Hayat;Shan Xue","doi":"10.1109/TBDATA.2025.3566617","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3566617","url":null,"abstract":"Session-based recommendations have become increasingly important in social media platforms due to the dynamic and temporal nature of user interactions. The utilization of Graph Neural Networks in these systems has grown due to their proficiency in incorporating node information and structural topology. However, current recommendation methods that use graphs focus on recommendations within a single session, neglecting the more complex dependencies between different sessions. This omission limits improvements in the accuracy of recommendations. In addition, existing GNN-based approaches generally focus on simple binary connections, neglecting to capture the intricate and heterogeneous interactions in real-world situations. Furthermore, a notable obstacle arises from the absence of node positional information for hyperedges in hypergraphs. Therefore, different item orders can lead to identical hyperedges, which limits the formation of precise session vector representations. The paper proposes a unified framework utilizing heterogeneous hypergraph neural networks for session-based social recommendations to address these limitations. This framework utilizes hypergraphs to depict complex multivariate connections among sessions, social networks, and items. It addresses the problem of hyperedge ambiguity while maintaining the sequential order of data. The methodology entails creating a linkage graph and a session-item graph, which aid in identifying similar user intentions across various sessions and potential behavior patterns within a single session. In addition, the framework utilizes a Graph Attention Network (GAT) to combine social information from users and their connections, thereby improving the representation of user interests. Empirical assessments on three datasets show that our proposed model outperforms popular recommendation models. This emphasizes its effectiveness in accurately capturing user preferences and behaviors in session-based social recommendations.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"2987-3002"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148478799","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":"Diffusion-Based Adversarial Purification With Feature Distillation","authors":"Sitong Liu;Zhichao Lian;Liang Xiao","doi":"10.1109/TBDATA.2025.3594243","DOIUrl":"https://doi.org/10.1109/TBDATA.2025.3594243","url":null,"abstract":"Adversarial purification is a defense strategy that utilizes generative models to neutralize adversarial perturbations. Diffusion models stand out for their powerful generative ability, making them the latest choice for generative models in adversarial purification methods. It is crucial to keep the balance between robustness against attacks and the integrity of the image content. However, these diffusion models are typically trained exclusively on clean data. We experimentally demonstrate that in the adversarial purification task, the data distribution operated by the diffusion model is often contaminated by adversarial perturbations. Adjusting the diffusion length to effectively mask adversarial perturbations while preserving image label semantics proves challenging. In this paper, we propose an innovative distillation-based diffusion approach for adversarial purification, enabling the diffusion model to operate effectively on the contaminated data distribution. Our approach involves a novel training process that integrates adversarial samples into the diffusion model's training by considering adversarial perturbations as part of the diffusion noise predicted by the model. The key to resisting perturbations is to align the feature representation of the purified image more closely with that of the clean image. To accomplish this, we develop a feature distillation technique to empower the diffusion model to learn and extract clean feature representations from adversarial samples. Extensive experimentation shows that our method achieves state-of-the-art performance against various adaptive attack benchmarks.","PeriodicalId":13106,"journal":{"name":"IEEE Transactions on Big Data","volume":"11 6","pages":"3383-3394"},"PeriodicalIF":8.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479758","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}