IEEE Transactions on Knowledge and Data Engineering最新文献

{"title":"Federated Incomplete Multi-View Clustering With Cross-View Relationship Imputation","authors":"Shuaijun Wang;Hui Huang;Nan Zhang;Shiliang Sun","doi":"10.1109/TKDE.2026.3692681","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3692681","url":null,"abstract":"Deep learning-based multi-view clustering techniques have attracted considerable attention due to their ability to recover missing views in incomplete multi-view scenarios. Nevertheless, in the federated multi-view learning scenario, these techniques are often constrained by the inherently decentralized nature of the data. Consequently, most existing federated incomplete multi-view methods primarily rely on internal correlations within a single view to recover missing data, failing to leverage complex global cross-view dependencies. This inherent limitation renders them particularly vulnerable to high missing rates, leading to a sharp decline in clustering accuracy and substantially restricting their applicability in complex, real-world scenarios. To address this issue, we propose the Federated Incomplete Multi-view clustering framework with Cross-view relationship Imputation, termed FIMCI. Specifically, we employ a Transformer-based encoder at each client and server to capture cross-view relationships, thereby completing missing data recovery and extracting view-specific information. We then design a dynamic view-fusion mechanism at the server, which adaptively assigns view weights and provides feedback to the clients. Furthermore, we implement category-level contrastive learning to enhance the robustness of the consensus representation through pseudo-label generation. In this way, FIMCI explores the consistency and complementarity between views through global view weight allocation and local view encoders, enabling the completion of missing views and clustering tasks while better protecting data privacy. Experimental results on multiple multi-view datasets verify that our method outperforms existing advanced methods in terms of both performance and efficiency.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4344-4356"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513068","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 Strength Estimation in Hyperbolic Space for Social Influence Maximization","authors":"Hongliang Qiao;Shanshan Feng;Min Zhou;Xutao Li;Yunming Ye;Fan Li;Shuo Shang;Yew-Soon Ong","doi":"10.1109/TKDE.2026.3683562","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3683562","url":null,"abstract":"The Influence Maximization (IM) problem aims to identify a small set of influential users, as seed users, to maximize their influence spread in a social network. Recently, graph representation learning approaches have gained wide attention in the IM field for their ability to encode social influence patterns into user representations, which are then used by various strategies to identify target seed users. While effective, these graph learning-based IM methods face two main limitations. First, they fail to model the influence propagation process explicitly, limiting their ability to capture the essential underlying propagation patterns. Second, they build representations in euclidean space, which cannot reflect the latent hierarchical structure of social influence distribution. As a result, the learned representations are ineffective in supporting seed user selection. To address these limitations, we propose a novel hyperbolic learning-based IM method, HIM, which leverages hyperbolic representation learning to estimate users’ influence strength from social data, particularly historical propagation processes, for solving IM tasks. Unlike previous approaches, HIM comprises two key components. First, Hyperbolic Influence Representation encodes influence spread patterns from both the social network and influence propagation instances into hyperbolic user representations. When learning from these data sources, the geometric properties of hyperbolic space naturally place highly influential users closer to the space origin, enabling practical estimation of influence strength from the distances of learned representations. Second, Adaptive Seed Selection introduces a novel scoring mechanism grounded in estimated influence strength. It leverages the geometric advantages of hyperbolic space to incrementally refine scores using multiple types of hyperbolic distance information, enabling flexible and effective seed user selection. Extensive experiments on five network datasets demonstrate the superior effectiveness and efficiency of our method under various diffusion models with both known and unknown propagation parameters, highlighting its potential for solving IM problems in large-scale, real-world social networks.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4434-4448"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513623","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":"Benefit From Noise: Detecting Time-Series Anomaly by Distinguishing Prior and Posterior Noises","authors":"Shiwang Xing;Jianwei Niu;Tao Ren;Joel J.P.C. Rodrigues","doi":"10.1109/TKDE.2026.3682883","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3682883","url":null,"abstract":"With the rapid development of digital technologies, a large range of real-world systems, spanning from cloud servers, IoT devices, to industrial control systems, continuously generate vast amounts of time series data. Time series anomaly detection (AD) plays a crucial role in maintaining system stability by identifying unusual patterns from normal distributions, with the primary challenge lies in learning effective anomaly-discriminative representations. Recently, diffusion models have been applied to time series AD due to their strong representational capabilities. However, existing diffusion-based methods typically rely on reconstruction errors, which not only fail to fully exploit the representational potential of diffusion models but also be computationally intensive. To address these limitations, through experimental observation and theoretical analysis, we show that <italic>specific regions of the diffusion noises exhibit stronger representation capabilities</i> for normal patterns, which can be leveraged to enhance AD performance and reduce computational costs. Building on these insights, we propose NoiseAD, a diffusion noise-guided anomaly detection method incorporating an optimal noise steps selection approach to identify diffusion steps with higher resolution. Extensive experiments on diverse benchmarks demonstrate the superiority of NoiseAD over state-of-the-art methods, further substantiated by insightful visualizations.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4107-4120"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508138","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":"Disentangled Graph Prompting for Out-Of-Distribution Detection","authors":"Cheng Yang;Yu Hao;Qi Zhang;Chuan Shi","doi":"10.1109/TKDE.2026.3678022","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3678022","url":null,"abstract":"When testing data and training data come from different distributions, deep neural networks (DNNs) will face significant safety risks in practical applications. Therefore, out-of-distribution (OOD) detection techniques, which can identify OOD samples at test time and alert the system, are urgently needed. Existing graph OOD detection methods usually characterize fine-grained in-distribution (ID) patterns from multiple perspectives, and train end-to-end graph neural networks (GNNs) for prediction. However, due to the unavailability of OOD data during training, the absence of explicit supervision signals could lead to sub-optimal performance of end-to-end encoders. To address this issue, we follow the <italic>pre-training+prompting</i> paradigm to utilize pre-trained GNN encoders, and propose Disentangled Graph Prompting (DGP), to capture fine-grained ID patterns with the help of ID graph labels. Specifically, we design two prompt generators that respectively generate class-specific and class-agnostic prompt graphs by modifying the edge weights of an input graph. We also design several effective losses to train the prompt generators and prevent trivial solutions. We conduct extensive experiments on ten datasets to demonstrate the superiority of our proposed DGP, which achieves a relative AUC improvement of 3.63% over the best graph OOD detection baseline. Ablation studies and hyper-parameter experiments further show the effectiveness of DGP.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4227-4238"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510384","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 Unified Perspective for Learning Graph Representations Across Multi-Level Abstractions","authors":"Mohamed Mahmoud Amar;Nairouz Mrabah;Mohamed Bouguessa;Abdoulaye Baniré Diallo","doi":"10.1109/TKDE.2026.3681811","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3681811","url":null,"abstract":"Graph Self-Supervised Learning (GSSL) has emerged as a powerful paradigm for generating high-quality representations for graph-structured data. While multi-scale graph contrastive learning has received increasing attention, many existing methods still predominantly focus on a single graph abstraction level. To address this limitation, we propose a unified contrastive framework that can target node-level, proximity-level, cluster-level, and graph-level information and integrate them through a linear combination of similarity scores on positive pairs and dissimilarity scores (i.e., similarity scores on negative pairs). Furthermore, current approaches typically assign uniform penalty strengths to all examples, which reduces optimization flexibility and leads to ambiguous convergence status. To overcome this, we introduce a novel parameter-free fine-grained self-weighting mechanism that adaptively assigns weights to individual similarity and dissimilarity scores. The proposed mechanism emphasizes the scores that deviate significantly from their target values. Our approach not only enhances optimization flexibility but also eliminates the computational overhead of hyperparameter tuning in conventional multi-task GSSL methods. Comprehensive experiments on real-world datasets show that our methods consistently outperform state-of-the-art approaches across downstream tasks, including classification, clustering, and link prediction, in both single-level and multi-level scenarios.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 7","pages":"4076-4092"},"PeriodicalIF":11.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512388","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":"Noise-Filtering Enhanced Graph Transformer for Robust Fake News Detection","authors":"Junyou Zhu;Chao Gao;Ze Yin;Xianghua Li;Zhen Wang;Jürgen Kurths","doi":"10.1109/TKDE.2026.3677544","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3677544","url":null,"abstract":"The rapid spread of fake news on social media has significantly increased the importance of computational detection methods. Graph-based approaches, particularly Graph Neural Networks (GNNs), have emerged as powerful tools for modeling news propagation patterns. Despite their potential, current GNN-based methods still face challenges in robustness and interpretability due to two key shortcomings: they inadequately filter out irrelevant user-induced noise within propagation graphs, and their shallow architectures fail to effectively capture the intricate long-range dependencies characteristic of news propagation. To overcome these limitations, we propose NEGT (Noise-filtering Enhanced Graph Transformer), a novel graph Transformer framework explicitly designed for fake news detection. NEGT introduces a noise-augmented information bottleneck strategy embedded within its self-attention mechanism, effectively identifying and removing task-irrelevant interactions. Additionally, we propose a novel relational propagation graph encoding a strategy that explicitly captures multi-scale user relationships and propagation depth, enabling NEGT to model long-sequence propagation dependencies accurately. Experiments on various benchmark datasets show that NEGT surpasses current methods in accuracy, noise robustness, and interpretability.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 6","pages":"3778-3791"},"PeriodicalIF":11.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510969","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":"FNES: Formulating Natural World Rules via Equiangular Spirals to Strengthen Temporal Knowledge Representation","authors":"Jiujiang Guo;Mankun Zhao;Tianyi Xu;Jianhang Song;Mei Yu;Linying Xu;Ruiguo Yu","doi":"10.1109/TKDE.2026.3666470","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3666470","url":null,"abstract":"Existing temporal knowledge graph completion research often extracts features from isolated aspects of temporal facts, which hinders modeling of multi-faceted semantic structures under complex temporal dynamics. This work begins with dynamic characteristics and behavior patterns of entities under independent perturbations of relations or time, and reveals following scenarios: <i>1) The sensitivity of relations to temporal changes:</i> Different relations between entities show distinct temporal sensitivities as time evolves. <i>2) Latent perturbations between relations:</i> Different relations mutually perturb and interact, revealing latent internal associations. <i>3) Snail shell-like time growth effect:</i> Timestamps can be modeled as outward growth of a spiral shell, where each spiral marks a trace of progress, reflecting cumulative and progressive nature of time. To address this, we design a novel model, FNES, inspired by natural structure of snail shells, which maps facts onto corresponding equiangular spirals. Specifically, FNES treats each entity as initial radial distance, maps each relation to a unique rotation angle, and uses each timestamp as a constant controlling rate of radial expansion. As relations gradually extend and timestamps expand outward, spiral structure progressively takes shape. To prevent intersections between spiral trajectories constructed by different entities, FNES learns modulus distribution differences between head and tail entities along <inline-formula><tex-math>$z$</tex-math></inline-formula>-axis. In this way, we provide a compact and coherent encoding structure for facts while simulating real-world rules to the greatest extent possible. Empirically, FNES significantly outperforms SOTA models on six public benchmarks as well as industrial mining datasets.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 6","pages":"3626-3639"},"PeriodicalIF":11.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510972","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":"Evolutionary Game Dynamics in Tripartite Crowdsourcing With Incentive Regulation","authors":"Zishuai Zhu;Shijia Hua;Linjie Liu;Xiaojie Chen","doi":"10.1109/TKDE.2026.3674095","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3674095","url":null,"abstract":"Crowdsourcing, which decomposes tasks or projects through the internet platform and outsources them to a large number of people, has gained significant academic and industry interest due to its efficiency. Although prior theoretical studies have employed evolutionary game theory to study crowdsourcing systems, existing studies have predominantly focused on the game interactions between crowdsourcing worker and requester, often neglecting the platform as a stakeholder. Here we propose an evolutionary game model for tripartite crowdsourcing, accounting for bounded rationality and potential collusion between platform and crowdsourcing worker, with incentives designed to counteract free-riding and deceptive feedback. Our theoretical results indicate that the system can achieve a desired state where workers provide high-quality work, the platform always implements diligent monitoring, and requesters adopt integrity strategies. Moreover, when the platform consistently adopts the monitoring strategy, the system exhibits oscillatory dynamics, with the probabilities of the worker providing high-quality work and the requester adopting an integrity strategy fluctuating within a bounded interval. Finally, we present numerical examples to validate our theoretical results.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 6","pages":"3556-3568"},"PeriodicalIF":11.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148511578","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":"Fairness-Aware Hypergraph Self-Supervised Learning With Sampling-Efficient Signals","authors":"Fan Li;Xiaoyang Wang;Dawei Cheng;Ying Zhang;Wenjie Zhang;Xuemin Lin","doi":"10.1109/TKDE.2026.3676748","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3676748","url":null,"abstract":"Self-supervised learning (SSL) provides a promising paradigm for hypergraph representation learning without reliance on costly labels. However, existing hypergraph SSL methods predominantly employ contrastive learning with instance-level discrimination, encountering two significant challenges: (1) Unreliable negative sampling, where arbitrarily selected negative samples introduce bias by misclassifying similar and dissimilar pairs; and (2) High computational cost, as effective training requires a large number of negative samples. To address these limitations, we propose SE-HSSL, a hypergraph SSL framework with three sampling-efficient self-supervised objectives. Specifically, two sampling-free objectives based on canonical correlation analysis serve as node- and group-level signals, while a hierarchical membership-level contrastive objective exploits the cascading overlap structure of hypergraphs. Beyond these challenges, deep hypergraph models are prone to biased predictions against groups defined by sensitive attributes (e.g., gender and race). We theoretically show that imbalanced group contributions during hypergraph message passing amplify such biases. To address this, we propose FairHSSL, a fairnessaware extension of SE-HSSL with a two-level debiasing augmentation strategy. Specifically, we construct a fair hypergraph view via complementary feature- and structure-level adjustments. At the feature level, orthogonal projection removes sensitive information from node representations; at the structure level, rebalance-based perturbation equalizes group contributions during message passing. By aligning the fair and original views under SSL, the model mitigates bias while preserving informative signals. Extensive experiments on 10 real-world hypergraphs demonstrate the effectiveness and efficiency of SE-HSSL.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 6","pages":"3611-3625"},"PeriodicalIF":11.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513343","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":"STABLE: Efficient Hybrid Nearest Neighbor Search via Magnitude-Uniformity and Cardinality-Robustness","authors":"Qianyun Yang;Zhiwei Chen;Yupeng Hu;Zixu Li;Zhiheng Fu;Liqiang Nie","doi":"10.1109/TKDE.2026.3676465","DOIUrl":"https://doi.org/10.1109/TKDE.2026.3676465","url":null,"abstract":"Hybrid Approximate Nearest Neighbor Search (Hybrid ANNS) is a foundational search technology for large-scale heterogeneous data and has gained significant attention in both academia and industry. However, current approaches overlook the heterogeneity in data distribution, thus ignoring two major challenges: the <italic>Compatibility Barrier for Similarity Magnitude Heterogeneity</i> and the <italic>Tolerance Bottleneck to Attribute Cardinality</i>. To overcome these issues, we propose the robu<underline>S</u>t he<underline>T</u>erogeneity-<underline>A</u>ware hy<underline>B</u>rid retrieva<underline>L</u> fram<underline>E</u>work, STABLE, designed for accurate, efficient, and robust hybrid ANNS under datasets with various distributions. Specifically, we introduce an enhAnced heterogeneoUs semanTic perceptiOn (AUTO) metric to achieve a joint measurement of feature similarity and attribute consistency, addressing similarity magnitude heterogeneity and improving robustness to datasets with various attribute cardinalities. Thereafter, we construct our Heterogeneous sEmantic reLation graPh (HELP) index based on AUTO to organize heterogeneous semantic relations. Finally, we employ a novel Dynamic Heterogeneity Routing method to ensure an efficient search. Extensive experiments on five feature vector benchmarks with various attribute cardinalities demonstrate the superior performance of STABLE.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"38 6","pages":"3927-3944"},"PeriodicalIF":11.6,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510725","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}