IEEE Transactions on Knowledge and Data Engineering最新文献

{"title":"Evidential Reliable Fusion for Partial Multi-View Incomplete Multi-Label Classification","authors":"Jiaying Zhou;Wai Keung Wong;Jiang Long;Xiaohuan Lu;Youliang Tian;Jie Wen","doi":"10.1109/TKDE.2026.3689103","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3689103","url":null,"abstract":"In recent years, the missing data problem in multi-view multi-label classification (MvMlC) has attracted extensive attention from researchers, with numerous solutions for partial multi-view incomplete multi-label classification (PMvIMlC) emerging. Nevertheless, two critical challenges persist. One is suboptimal coarse-grained multi-view fusion: traditional dynamic fusion at the view level is unable to accommodate the practical fusion demands of samples with diverse qualities. The other is neglecting latent information within missing labels: during the training phase, existing works only focus on the limited supervised information of unmissing labels while ignoring the underlying information at missing positions. To address these issues, we propose Evidential Reliable Fusion for Partial Multi-view Incomplete Multi-label Classification, termed <bold>ERF</b>. ERF comprises two core modules: 1) Uncertainty-guided fusion module via evidence theory and 2) adaptive negative label pseudo-labeling. The former quantifies sample-level uncertainty of each view based on evidence theory, which is then used to guide multi-view fusion, enabling a fine-grained, instance-level multi-view fusion scheme. For the latter, leveraging the model’s perception ability for neighboring samples in the label space, we design a strategy to select reliable negative pseudo-labels. This module enhances supervisory information to aid model training by recovering reliable negative pseudo-labels. Extensive experiments demonstrate that our ERF delivers significantly superior classification performance over existing methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4330-4343"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512390","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":"Enhancing Federated Domain Generalization by Data Influences on Global Model Update","authors":"Wen Huang;Zikun Zhou;WeiXin Zhao;Xingyi Wang;Zhishuo Zhang;Jian Peng","doi":"10.1109/TKDE.2026.3691040","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3691040","url":null,"abstract":"With the popularity of federated learning, federated domain generalization (FedDG) has attracted more and more attention. Existing works of federated learning indicate that the generalization performance of the global model can be improved when the global model is obtained by aggregating local models according to suitable weights. However, existing methods to calculate weights do not fully utilize the data influences on the global model update, which gives us an opportunity to improve the generalization performance of the global model further. In this paper, we propose the method DI (data influences), which utilizes data influences on the global model update to calculate dynamical weights of local model in each round of training. Specifically, the first component data influence calculator (DIC) of DI calculates local weights of local model from the influences of data on the global model update and we introduce the influence function to complete the calculation process. The second component data influence adjuster (DIA) of DI calculates global weights (which are used in the aggregation process of the global model) from local weights. Extensive experiments indicate that our method improves the generalization performance of models significantly. In particular, our method improves model accuracy on benchmark datasets PACS, OfficeHome, and Office-31 by 1.79%, 1.61%, and 2.39% on average, respectively.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4287-4300"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513062","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":"Influence Persistence Maximization in Temporal Social Networks","authors":"Xueqin Chang;Qing Liu;Baihua Zheng;Yunjun Gao","doi":"10.1109/TKDE.2026.3690643","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3690643","url":null,"abstract":"In this paper, we investigate a novel <u>Inf</u>luence <u>P</u>ersistence <u>M</u>aximization (<small>InfPM</small>) problem in temporal social networks. Given a temporal graph, <small>InfPM</small> aims to identify a fixed seed node set <inline-formula><tex-math>$S$</tex-math></inline-formula> that maximizes the total duration of persistent influence across consecutive snapshots. After proving that <small>InfPM</small> is NP-hard, monotonic, and non-submodular, we develop two efficient solutions: (1) <inline-formula><tex-math>${sf RevG}$</tex-math></inline-formula>, a reverse greedy algorithm that iteratively removes low-contribution nodes, and (2) <inline-formula><tex-math>${sf LRep}$</tex-math></inline-formula>, a replacement-based method that progressively improves the quality of seed node set. To accelerate influence computation in <inline-formula><tex-math>${sf RevG}$</tex-math></inline-formula> and <inline-formula><tex-math>${sf LRep}$</tex-math></inline-formula>, we propose a new influence computation method integrating snapshot compression, probability-aware sampling, and a specialized influence estimator offering unbiased estimation. Additionally, we explore a practical variant of <small>InfPM</small>, termed <small>Win-InfPM</small>, which relaxes the requirement of consecutive snapshots by introducing a flexible time window model. Extensive experiments on seven real-world networks demonstrate that (1) <inline-formula><tex-math>${sf RevG}$</tex-math></inline-formula> and <inline-formula><tex-math>${sf LRep}$</tex-math></inline-formula> effectively identify high-quality seed nodes, achieving up to 100% improvement in total influence persistence over the baselines; and (2) the proposed influence computation method improves the efficiency of <inline-formula><tex-math>${sf RevG}$</tex-math></inline-formula> and <inline-formula><tex-math>${sf LRep}$</tex-math></inline-formula> by up to 400%, while maintaining comparable influence persistence.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4419-4433"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512416","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":"Graph Neural Networks for Graphs With Heterophily: A Survey","authors":"Xin Zheng;Yi Wang;Yixin Liu;Ming Li;Miao Zhang;Di Jin;Philip S. Yu;Shirui Pan","doi":"10.1109/TKDE.2026.3680353","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3680353","url":null,"abstract":"Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriad graph analytic tasks and applications. Most GNNs rely on the homophily assumption that nodes belonging to the same class are more likely to be connected. However, as a ubiquitous graph property in numerous real-world scenarios, heterophily, i.e., nodes with different labels tend to be linked, significantly limits the performance of tailor-made homophilic GNNs. Hence, <italic>GNNs for heterophilic graphs</i> are gaining increasing research attention to enhance graph learning with heterophily. In this paper, we provide a comprehensive review of GNNs for heterophilic graphs. Specifically, we propose a systematic taxonomy that governs existing heterophilic GNN models, along with general summaries and detailed analyses. Furthermore, we discuss the relationship between heterophily and various graph research domains, aiming to facilitate the development of more effective GNNs across a spectrum of practical applications and learning tasks in the graph research community. In the end, we point out potential directions to advance and inspire future research and applications on heterophilic graph learning with GNNs.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4385-4404"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148511020","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":"Cog-RMH: Cognition-Based Recalling Multiview History for Event Forecasting in Temporal Knowledge Graph","authors":"Feng Zhao;Kangzheng Liu;Yu Yang;Guandong Xu","doi":"10.1109/TKDE.2026.3689542","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3689542","url":null,"abstract":"Event forecasting is an important task in temporal knowledge graphs (TKGs), aiming to leverage historical memories like humans to make informed decisions about future unknown events. However, according to the multiple intelligence and encoding-retrieval specificity theories, the previous approaches conflict with two fundamental human decision-making paradigms, resulting in incomplete representations of historical events over TKGs and thereby hindering later future forecasting performance. On the one hand, humans recall memories from multiple perspectives, while previous event forecasting works have unilaterally viewed historical sequences as a single-perspective paradigm, resulting in incomplete learning of structural features. On the other hand, the human memory recollection (recall) is a bidirectional process. Existing approaches merely focus on capturing unidirectional evolutionary patterns in chronological order, failing to properly model the time-varying retracing–retrieval processes of memory recollection over time. In this paper, we propose a novel event forecasting method in TKGs, namely <bold>Cog-RMH</b>, mimicking the human <underline><b>Cog</b></u>nitive paradigm and <underline><b>R</b></u>ecalling <underline><b>M</b></u>ultiview <underline><b>H</b></u>istory to support future decision making. To address the former challenge, we extract inherent structural features of historical concurrent events derived from associative thinking (amygdala), spatial context (hippocampus), and logical reasoning (prefrontal cortex) intelligences, and synthesize their effects on future events. To tackle the latter issue, we propose an encoder-decoder architecture with stacked gated recurrent units that simulate bidirectional memory retracing and retrieval containing cognitive dependencies. We further introduce a retracing–retrieval attention mechanism to model the time-varying human emphasis on different timestamp events during recall. Extensive experiments show that Cog-RMH achieves significantly improved event forecasting performance on four public TKG benchmarks in comparison with the existing state-of-the-art baselines.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4170-4184"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11503463","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148511691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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":"Beyond Predefined Clusters: A Comprehensive Review of Clustering Methods for Unknown Numbers of Clusters","authors":"Nazila Pourhaji Aghayengejeh;M. A. Balafar;Jafar Tanha;M. Alper Selver","doi":"10.1109/TKDE.2026.3680286","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3680286","url":null,"abstract":"Clustering is an unsupervised learning task that groups data points by their inherent similarities. Non-automatic clustering algorithms face significant challenges when the true number of clusters is unknown or changes dynamically, as they require this number to be predefined. This paper provides a comprehensive review of automatic clustering algorithms specifically designed to handle such uncertainty. In this paper, these algorithms are systematically classified based on three key perspectives: clustering framework (classical vs. deep), clustering strategy (e.g., density-based, model based, graph-theoretic, subspace methods), and the use of labeled data (unsupervised vs. semi-supervised). We analyze each algorithm based on its core principles, key contributions, strengths, and limitations. Furthermore, we address the current challenges in this area and propose future research directions to enhance the scalability, robustness, and effectiveness of automatic clustering algorithms.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4121-4138"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513624","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":"CuIoT: Advancing Network Connectivity With Motif Knowledge-Centric for Robust Topology","authors":"Ning Chen;Songwei Zhang;Xiaobo Zhou;Xiaochen Huang;Dapeng Oliver Wu;Tie Qiu","doi":"10.1109/TKDE.2026.3677426","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3677426","url":null,"abstract":"The robustness of intelligent IoT device networking is vital for maintaining communication connectivity within intelligent manufacturing systems, impacting the reliability of the customized Industrial Internet of Things (CuIoT). Current studies enhance network connectivity and resilience against cyber attacks through combinatorial optimization theory by redeploying topologies. However, these approaches often overlook the transformative potential of network motifs in the optimization process. To address this, we introduce CuIoT-MET, an innovative approach that enhances CuIoT robustness by leveraging motif evolutionary transfer knowledge from historical evolution processes. By analyzing changes in connection relationships and emphasizing network motifs’ unique contributions, we design a novel robustness metric to optimize the evolutionary trajectory, resulting in more robust CuIoT connection patterns. Extensive experiments show that CuIoT-MET outperforms state-of-the-art methods in improving network robustness.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4213-4226"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510334","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":"DynEformer: A Unified Framework for Robust Workload Prediction Under Dynamic Environment","authors":"Shaoyuan Huang;Zheng Wang;Heng Zhang;Xiaofei Wang;Cheng Zhang;Wenyu Wang","doi":"10.1109/TKDE.2026.3689812","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3689812","url":null,"abstract":"Workload prediction in multi-tenant edge cloud platforms (MT-ECP) is crucial for efficient application deployment and resource provisioning. However, the heterogeneous application patterns, variable infrastructure performance, and frequent deployments in MT-ECP pose significant challenges for accurate prediction. Existing clustering-based methods often incur excessive costs due to maintaining multiple data clusters and models, while end-to-end time-series prediction methods struggle with dynamic environments. To address these challenges, we perform a comprehensive analysis on a large-scale workload dataset in real-world MT-ECP and propose DynEformer, an end-to-end framework with global pooling and static context awareness, offering a unified workload prediction scheme for dynamic MT-ECP. Meticulously designed global pooling and information merging mechanisms can effectively identify and utilize global application patterns to drive local workload predictions. The integration of static content-aware mechanisms enhances model robustness in real-world scenarios. We also extend DynEformer’s capabilities to Long-term workload forecasting (LTLF) and Long-period service (LPS) tasks. Experiments on six real-world datasets demonstrate that DynEformer achieves state-of-the-art performance, with a 32% relative improvement on nine baselines and a 52% improvement in application switching and new entity scenarios. Additional experiments on long-term prediction and online learning further confirm its effectiveness for LTLF and LPS tasks.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4239-4253"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512268","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":"Enhancing Large Language Models Reasoning via Multi-Path Optimization on Knowledge Graph","authors":"Jiyong Liao;Chubo Liu;Yan Ding;Haotian Wang;Zhuo Tang;Kenli Li;Keqin Li","doi":"10.1109/TKDE.2026.3682046","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3682046","url":null,"abstract":"Large language models (LLMs) have demonstrated remarkable reasoning and generation capabilities in various natural language tasks. However, they often struggle with hallucinations or reasoning errors, particularly when handling domain-specific knowledge or complex multi-hop reasoning. The integration of knowledge graph (KG) provides LLMs with structured and reliable contextual knowledge, effectively mitigating issues of factual accuracy and incomplete reasoning chains. Nevertheless, existing KG-guided LLM reasoning methods still face challenges, including narrow answer coverage, limited accuracy in multi-hop reasoning, and inefficiency caused by frequent LLM API calls. To address these problems, we propose ELMK (Enhancing Large language models reasoning via Multi-path optimization on Knowledge graph), a novel KG-based LLM reasoning method that improves output comprehensiveness and interpretability. ELMK follows a retrieval–embedding–reasoning pipeline. First, depth-first search is used to extract relevant reasoning graphs, then a multi-path encoder is trained to semantically encode the question and candidate paths for precise path selection. Subsequently, the multi-path exploration strategy divides paths into multiple semantic clusters and selects the most similar paths from each cluster to ensure diverse coverage and complete answers. Finally, these paths are combined with prompts to guide LLMs toward reliable outputs. Extensive experiments on public benchmarks demonstrate that ELMK outperforms several state-of-the-art methods in terms of performance and generates more faithful and interpretable reasoning results.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4301-4314"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513064","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":"Ensemble Graph Neural Networks With Individual Decision Feedback for Graph Classification","authors":"Mingjian Guang;Zhong Li;Rui Zhang;Junli Wang;Dawei Cheng","doi":"10.1109/TKDE.2026.3691148","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3691148","url":null,"abstract":"Graph classification is a classic data mining task in graph-related domains. Graph Neural Networks (GNNs) have emerged as essential methods for graph classification due to their powerful ability to extract knowledge from graph-level data. Most methods employ a single GNN to learn graph representations and may overfit small datasets. Some ensemble graph neural networks combine multiple independent GNNs as an ensemble classifier, leveraging collective intelligence to provide more robust collective decisions to alleviate overfitting. Despite their effectiveness, they hardly consider and analyze the underlying implications behind the consistency and inconsistency of individual decisions. Moreover, concrete ensemble learning strategies for diverse individual decisions are currently lacking. Therefore, we propose an ensemble graph neural network with individual decision feedback that efficiently combines multiple GNNs for graph classification. In particular, we conduct a detailed analysis of the potential information fed back from individual decisions and propose an individual decision feedback weighting method to dynamically adjust model training, encouraging GNNs to focus more on the crucial graphs for training. Besides, we design a credibility-evaluation-based decision fusion method to fuse the most reliable individual decisions as the collective decision. Finally, we evaluate the performance of our method by conducting extensive experiments and demonstrating its effectiveness.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4315-4329"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513059","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}