IEEE Transactions on Knowledge and Data Engineering最新文献

{"title":"CloudRGK: Towards Private Similarity Measurement Between Graphs on the Cloud","authors":"Linxiao Yu;Jun Tao;Yifan Xu;Haotian Wang","doi":"10.1109/TKDE.2025.3529949","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3529949","url":null,"abstract":"Graph kernels are a significant class of tools for measuring the similarity of graph data, which is the basis of a wide range of graph learning methods. However, graph kernels often suffer from high computing overhead. With the shining of cloud computing, it is desirable to transfer the computing burden to the server with abundant computing resources to reduce the cost of local machines. Nonetheless, under the honest-but-curious cloud assumption, the server may peek at the data, raising privacy concerns. To eliminate the risk of data privacy leakage, we propose CloudRGK to securely perform Random walk Graph Kernel(RGK), one of the most well-known graph kernels, on the cloud. We first prove that the edge- and vertex-labeled graphs could be transformed into an equivalent matrix representation. Afterward, we prove that the cloud could perform the core operations in RGK on the encrypted graphs without feature information loss. Evaluations of the real-world graph data demonstrate that our strategy significantly reduces the overhead of the local party to perform RGK without performance degradation. Meanwhile, it introduces only a small amount of extra computation cost. To the best of our knowledge, it is the first work towards private graph kernel computation on the cloud.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1688-1701"},"PeriodicalIF":8.9,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570835","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}

{"title":"A Generalized $f$f-Divergence With Applications in Pattern Classification","authors":"Fuyuan Xiao;Weiping Ding;Witold Pedrycz","doi":"10.1109/TKDE.2025.3530524","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3530524","url":null,"abstract":"In multisource information fusion (MSIF), Dempster–Shafer evidence (DSE) theory offers a useful framework for reasoning under uncertainty. However, measuring the divergence between belief functions within this theory remains an unresolved challenge, particularly in managing conflicts in MSIF, which is crucial for enhancing decision-making level. In this paper, several divergence and distance functions are proposed to quantitatively measure discrimination between belief functions in DSE theory, including the reverse evidential KullbackLeibler (REKL) divergence, evidential Jeffrey’s (EJ) divergence, evidential JensenShannon (EJS) divergence, evidential <inline-formula><tex-math>$chi ^{2}$</tex-math></inline-formula> (E<inline-formula><tex-math>$chi ^{2}$</tex-math></inline-formula>) divergence, evidential symmetric <inline-formula><tex-math>$chi ^{2}$</tex-math></inline-formula> (ES<inline-formula><tex-math>$chi ^{2}$</tex-math></inline-formula>) divergence, evidential triangular (ET) discrimination, evidential Hellinger (EH) distance, and evidential total variation (ETV) distance. On this basis, a generalized <inline-formula><tex-math>$f$</tex-math></inline-formula>-divergence, also called the evidential <inline-formula><tex-math>$f$</tex-math></inline-formula>-divergence (Ef divergence), is proposed. Depending on different kernel functions, the Ef divergence degrades into several specific classes: EKL, REKL, EJ, EJS, E<inline-formula><tex-math>$chi ^{2}$</tex-math></inline-formula> and ES<inline-formula><tex-math>$chi ^{2}$</tex-math></inline-formula> divergences, ET discrimination, and EH and ETV distances. Notably, when basic belief assignments (BBAs) are transformed into probability distributions, these classes of Ef divergence revert to their classical counterparts in statistics and information theory. In addition, several Ef-MSIF algorithms are proposed for pattern classification based on the classes of Ef divergence. These Ef-MSIF algorithms are evaluated on real-world datasets to demonstrate their practical effectiveness in solving classification problems. In summary, this work represents the first attempt to extend classical <inline-formula><tex-math>$f$</tex-math></inline-formula>-divergence within the DSE framework, capitalizing on the distinct properties of BBA functions. Experimental results show that the proposed Ef-MSIF algorithms improve classification accuracy, with the best-performing Ef-MSIF algorithm achieving an overall performance difference approximately 1.22 times smaller than the suboptimal method and 14.12 times smaller than the worst-performing method.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1556-1570"},"PeriodicalIF":8.9,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570592","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}

{"title":"EA$^{2}$2N: Evidence-Based AMR Attention Network for Fake News Detection","authors":"Shubham Gupta;Abhishek Rajora;Suman Kundu","doi":"10.1109/TKDE.2025.3529707","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3529707","url":null,"abstract":"Proliferation of fake news has become a critical issue in today's information-driven society. Our study includes external knowledge from Wikidata which allows the model to cross-reference factual claims with established knowledge. This approach deviates from the reliance on social information to detect fake news that many state-of-the-art (SOTA) fact-checking models adopt. This paper introduces <b>EA<inline-formula><tex-math>$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula>N</b>, an <b>E</b>vidence-based <b>A</b>MR (abstract meaning representation) <b>A</b>ttention <b>N</b>etwork for Fake News Detection. EA<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>N utilizes the proposed Evidence based Abstract Meaning Representation (WikiAMR) which incorporates knowledge using a proposed evidence-linking algorithm, pushing the boundaries of fake news detection. The proposed framework encompasses a combination of a novel language encoder and a graph encoder to detect fake news. While the language encoder effectively combines transformer-encoded textual features with affective lexical features, the graph encoder encodes semantic relations with evidence through external knowledge, referred to as WikiAMR graph. A path-aware graph learning module is designed to capture crucial semantic relationships among entities over evidence. Extensive experiments support our model's superior performance, surpassing SOTA methodologies with a difference of 2-3% in F1-score and accuracy for Politifact and Gossipcop datasets. The improvement due to the introduction of WikiAMR is found to be statistically significant with t-value less than 0.01.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1742-1752"},"PeriodicalIF":8.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570800","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}

{"title":"MHR: A Multi-Modal Hyperbolic Representation Framework for Fake News Detection","authors":"Shanshan Feng;Guoxin Yu;Dawei Liu;Han Hu;Yong Luo;Hui Lin;Yew-Soon Ong","doi":"10.1109/TKDE.2025.3528951","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3528951","url":null,"abstract":"The rapid growth of the internet has led to an alarming increase in the dissemination of fake news, which has had many negative effects on society. Various methods have been proposed for detecting fake news. However, these approaches suffer from several limitations. First, most existing works only consider news as separate entities and do not consider the correlations between fake news and real news. Moreover, these works are usually conducted in the Euclidean space, which is unable to capture complex relationships between news, in particular the hierarchical relationships. To tackle these issues, we introduce a novel <underline>M</u>ulti-modal <underline>H</u>yperbolic <underline>R</u>epresentation framework (MHR) for fake news detection. Specifically, we capture the correlations between news for graph construction to arrange and analyze different news. To fully utilize the multi-modal characteristics, we first extract the textual and visual information, and then design a Lorentzian multi-modal fusion module to fuse them as the node information in the graph. By utilizing the fully hyperbolic graph neural networks, we learn the graph’s representation in hyperbolic space, followed by a detector for detecting fake news. The experimental results on three real-world datasets demonstrate that our proposed MHR model achieves state-of-the-art performance, indicating the benefits of hyperbolic representation.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"2015-2028"},"PeriodicalIF":8.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570802","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}

{"title":"Dual Enhanced Meta-Learning With Adaptive Task Scheduler for Cold-Start Recommendation","authors":"Dongxiao He;Jiaqi Cui;Xiaobao Wang;Guojie Song;Yuxiao Huang;Lingfei Wu","doi":"10.1109/TKDE.2025.3529525","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3529525","url":null,"abstract":"Recommendation systems typically rely on users’ historical behavior to infer their preferences. However, when new entries emerge, the system cannot make accurate prediction due to the lack of historical data. This is known as the “cold-start” problem, which not only limits the exposure of new items but also impacts the first experience of new users severely. Meta-learning has emerged as a promising approach to address this issue, but existing methods have limitations in dealing with the differences in user preferences and sparse monitoring data. To overcome these limitations, Dual enhanced Meta-learning with Adaptive Task Sampling is proposed. First, we propose an embedding enhancement strategy for cold nodes. Specifically, we map the cold-start embeddings into the warm space based on the common features shared across all nodes, and then add uniform noise to create the contrastive views. This strategy injects warm co-occurrence signals into the content of cold nodes, effectively enriching the feature space of cold nodes. Second, we introduce an adaptive task scheduler to measure the effectiveness of different meta-tasks and filter out the noise from invalid tasks. We assign different sampling probabilities to the tasks based on the learning process (gradient similarity) and the learning result (loss) of the meta-tasks. Finally, we consider the above two modules as auxiliary tasks for the main meta-model. Then, joint optimization is carried out through a multi-task learning framework. Experiments in three cold-start scenarios show that our approach outperforms the most advanced baselines, including traditional methods, HIN-based methods, and meta-learning-based methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1728-1741"},"PeriodicalIF":8.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570596","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}

{"title":"Spatio-Temporal Prediction on Streaming Data: A Unified Federated Continuous Learning Framework","authors":"Hao Miao;Yan Zhao;Chenjuan Guo;Bin Yang;Kai Zheng;Christian S. Jensen","doi":"10.1109/TKDE.2025.3528876","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3528876","url":null,"abstract":"The widespread deployment of wireless and mobile devices results in a proliferation of decentralized spatio-temporal data. Many recent proposals that target deep learning for spatio-temporal prediction assume that all data is available at a central location and suffers from so-called catastrophic forgetting, where previously learned knowledge is entirely forgotten when new data arrives. Such proposals may face data privacy concerns and may experience deteriorating prediction performance when applied in decentralized settings where data streams into the system. To bridge the gap between decentralized training and spatio-temporal prediction on streaming data, we propose a unified federated continuous learning framework, which uses a horizontal federated learning mechanism for protecting data privacy and includes a global replay buffer with synthetic spatio-temporal data generated by the previously learned global model. For each client, we fuse the current training data with synthetic spatio-temporal data using a spatio-temporal mixup mechanism to preserve historical knowledge effectively, thus avoiding catastrophic forgetting. To enable holistic representation preservation, the local models at clients each integrates a general spatio-temporal autoencoder with a spatio-temporal simple siamese network that aims to ensure prediction accuracy and avoid holistic feature loss. Extensive experiments on real data offer insight into the effectiveness of the proposed framework.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"2126-2140"},"PeriodicalIF":8.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570838","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}

{"title":"LLM-Driven Causal Discovery via Harmonized Prior","authors":"Taiyu Ban;Lyuzhou Chen;Derui Lyu;Xiangyu Wang;Qinrui Zhu;Huanhuan Chen","doi":"10.1109/TKDE.2025.3528461","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3528461","url":null,"abstract":"Traditional domain-specific causal discovery relies on expert knowledge to guide the data-based structure learning process, thereby improving the reliability of recovered causality. Recent studies have shown promise in using the Large Language Model (LLM) as causal experts to construct autonomous expert-guided causal discovery systems through causal reasoning between pairwise variables. However, their performance is hampered by inaccuracies in aligning LLM-derived causal knowledge with the actual causal structure. To address this issue, this paper proposes a novel LLM-driven causal discovery framework that limits LLM’s prior within a reliable range. Instead of pairwise causal reasoning that requires both precise and comprehensive output results, the LLM is directed to focus on each single aspect separately. By combining these distinct causal insights, a unified set of structural constraints is created, termed a harmonized prior, which draws on their respective strengths to ensure prior accuracy. On this basis, we introduce plug-and-play integrations of the harmonized prior into mainstream categories of structure learning methods, thereby enhancing their applicability in practical scenarios. Evaluations on real-world data demonstrate the effectiveness of our approach.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1943-1960"},"PeriodicalIF":8.9,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570613","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}

{"title":"Dynamic Ensemble Framework for Imbalanced Data Classification","authors":"Tuanfei Zhu;Xingchen Hu;Xinwang Liu;En Zhu;Xinzhong Zhu;Huiying Xu","doi":"10.1109/TKDE.2025.3528719","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3528719","url":null,"abstract":"Dynamic ensemble has significantly greater potential space to improve the classification of imbalanced data compared to static ensemble. However, dynamic ensemble schemes are far less successful than static ensemble methods in the imbalanced learning field. Through an in-depth analysis on the behavior characteristics of dynamic ensemble, we find that there are some important problems that need to be addressed to release the full potential of dynamic ensemble, including but not limited to, correcting the component classifiers’ bias towards the majority classes, increasing the proportions of the positive classifiers (i.e., the component classifiers making correct prediction) for difficult samples, and providing the accurate competence estimations on the hard-to-classify samples w.r.t the classifier pool. Inspired by these, we propose a Dynamic Ensemble Framework for imbalanced data classification (imDEF). imDEF first uses the data generation method OREM<inline-formula><tex-math>$mathrm{_{G}}$</tex-math></inline-formula> to generate multiple artificial synthetic datasets, which have diverse class distributions by rebalancing the original imbalanced data. Based on each of such synthetic datasets, imDEF then utilizes a Classification Error-aware Self-Paced Sampling Ensemble (SPSE<inline-formula><tex-math>$mathrm{_{CE}}$</tex-math></inline-formula>) method to gradually focus more on difficult samples, to create a low-biased classifier pool and increase the proportions of the positive classifiers for the difficult samples. Finally, imDEF constructs a referee system to achieve the competence estimations by leveraging an Ensemble Margin-aware Self-Paced Sampling Ensemble (SPSE<inline-formula><tex-math>$mathrm{_{EM}}$</tex-math></inline-formula>) method. SPSE<inline-formula><tex-math>$mathrm{_{EM}}$</tex-math></inline-formula> incrementally strengthens the learning of the hard-to-classify samples, so that the competent levels of component classifiers could be estimated accurately. Extensive experiments demonstrate the effectiveness of imDEF. The source codes have been made publicly available on GitHub.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2456-2471"},"PeriodicalIF":8.9,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769403","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}

{"title":"SemSI-GAT: Semantic Similarity-Based Interaction Graph Attention Network for Knowledge Graph Completion","authors":"Xingfei Wang;Ke Zhang;Muyuan Niu;Xiaofen Wang","doi":"10.1109/TKDE.2025.3528496","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3528496","url":null,"abstract":"Graph Neural Networks (GNNs) show great power in Knowledge Graph Completion (KGC) as they can handle non-Euclidean graph structures and do not depend on the specific shape or topology of the graph. However, many current GNN-based KGC models have difficulty in effectively capturing and utilizing the substantial structure and global semantic information in Knowledge Graphs (KGs). For more effective use of GNN for KGC, we innovatively propose the Semantic Similarity-based Interaction Graph Attention Network (SemSI-GAT) for the KGC task. In SemSI-GAT, we utilize BERT, a pre-trained language model, to learn the global semantic information and obtain semantic similarity between entities and their neighbors. Furthermore, we creatively design a novel encoder network called the interaction graph attention network and introduce a semantic similarity sampling mechanism to optimize the aggregation of interaction information between neighbors. By aggregating local features with interaction features, this network can generate more expressive structural embeddings. This network generates more expressive embeddings by fusing global semantic information, local structure features, and interaction features. The experimental evaluations demonstrate that the proposed SemSI-GAT outperforms existing state-of-the-art KGC methods on four benchmark datasets.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2958-2970"},"PeriodicalIF":8.9,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769518","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}

{"title":"Machine Unlearning Through Fine-Grained Model Parameters Perturbation","authors":"Zhiwei Zuo;Zhuo Tang;Kenli Li;Anwitaman Datta","doi":"10.1109/TKDE.2025.3528551","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3528551","url":null,"abstract":"Machine unlearning involves retracting data records and reducing their influence on trained models, aiding user privacy protection, at a significant computational cost potentially. Weight perturbation-based unlearning is common but typically modifies parameters globally. We propose fine-grained Top-K and Random-k parameters perturbed inexact machine unlearning that address the privacy needs while keeping the computational costs tractable. However, commonly used training data are independent and identically distributed, for inexact machine unlearning, current metrics are inadequate in quantifying unlearning degree that occurs after unlearning. To address this quantification issue, we introduce SPD-GAN, which subtly perturbs data distribution targeted for unlearning. Then, we evaluate unlearning degree by measuring the performance difference of the models on the perturbed unlearning data before and after unlearning. Furthermore, to demonstrate efficacy, we tackle the challenge of evaluating machine unlearning by assessing model generalization across unlearning and remaining data. To better assess the unlearning effect and model generalization, we propose novel metrics, namely, the forgetting rate and memory retention rate. By implementing these innovative techniques and metrics, we achieve computationally efficacious privacy protection in machine learning applications without significant sacrifice of model performance. A by-product of our work is a novel method for evaluating and quantifying unlearning degree.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1975-1988"},"PeriodicalIF":8.9,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570561","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}