IEEE Transactions on Knowledge and Data Engineering最新文献

{"title":"Adaptive Reliable Defense Graph for Multi-Channel Robust GCN","authors":"Xiao Zhang;Peng Bao","doi":"10.1109/TKDE.2025.3538645","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3538645","url":null,"abstract":"Graph Convolutional Networks (GCNs) have demonstrated remarkable success in various graph-related tasks. However, recent studies show that GCNs are vulnerable to adversarial attacks on graph structures. Therefore, how to defend against such attacks has become a popular research topic. The current common defense methods face two main limitations: (1) From the data perspective, it may lead to suboptimal results since the structural information is ignored when distinguishing the perturbed edges. (2) From the model perspective, the defenders rely on the low-pass filter of the GCN, which is vulnerable during message passing. To overcome these limitations, this paper analyzes the characteristics of perturbed edges, and based on this we propose a robust defense framework, <italic>REDE</i>, to generate the adaptive <italic>Re</i>liable <italic>De</i>fense graph for multi-channel robust GCN. REDE first uses feature similarity and structure difference to discriminate perturbed edges and generates the defense graph by pruning them. Then REDE designs a multi-channel GCN, which can separately capture the information of different edges and high-order neighbors utilizing different frequency components. Leveraging this capability, the defense graph is adaptively updated at each layer, enhancing its reliability and improving prediction accuracy. Extensive experiments on four benchmark datasets demonstrate the enhanced performance and robustness of our proposed REDE over the state-of-the-art defense methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2226-2238"},"PeriodicalIF":8.9,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769536","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":"Hypergraph Collaborative Filtering With Adaptive Augmentation of Graph Data for Recommendation","authors":"Jian Wang;Jianrong Wang;Di Jin;Xinglong Chang","doi":"10.1109/TKDE.2025.3539769","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3539769","url":null,"abstract":"Self-supervised tasks show significant advantages for node representation learning in recommender systems. This core idea of self-supervised task-based recommender systems depends on data augmentation to generate multi-view representations. However, there are two key challenges that are not well explored in existing self-supervised tasks: i) Restricted by the structure of the graph-based CF paradigm itself, the classical graph comparison learning architecture ignores the global structural information on the user-item interaction graph. ii) In a key part of existing contrast learning-random graph data enhancement schemes can significantly deteriorate model performance. To address these challenges, we propose a new hypergraph collaborative filtering with adaptive augmentation framework(HCFAA). It captures both local and global collaborative relationships on the user-item graph through a hypergraph-enhanced joint learning architecture. In particular, the designed adaptive structure-guided model ignores the noise introduced on unimportant edges, and thus learns the critical node information on the user-item graph. Comprehensive experimental studies on the Amazon dataset show that the method is effective, which provides an optimization scheme with a new perspective for the problems of key node loss in graph data enhancement and loss of higher-order structural information in GNN. The source code of our model can be available on <uri>https://github.com/RSnewbie/RS/tree/master/HCFAA</uri>.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2640-2651"},"PeriodicalIF":8.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769554","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":"GAFExplainer: Global View Explanation of Graph Neural Networks Through Attribute Augmentation and Fusion Embedding","authors":"Wenya Hu;Jia Wu;Quan Qian","doi":"10.1109/TKDE.2025.3539989","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3539989","url":null,"abstract":"The excellent performance of graph neural networks (GNNs), which learn node representations by aggregating their neighborhood information, led to their use in various graph tasks. However, GNNs are black box models, the prediction results of which are difficult to understand directly. Although node attributes are vital for making predictions, previous studies have ignored their importance for explanation. This study presents GAFExplainer, a novel GNN explainer that emphasizes node attributes via attribute augmentation and fusion embedding. The former enhances node attribute encoding for more expressive masks, while the latter preserves the discrimination of node representations across different layers. Together, these modules significantly improve explanation performance. By training the explanatory network, a global view explanation of GNN models is obtained, and reasonably explainable subgraphs are available for new graphs, thus rendering the model well-generalizable. Multiple sets of experimental results on real and synthetic datasets demonstrate that the proposed model provides valid and accurate explanations. In the visual analysis, the explanations obtained by the proposed model are more comprehensible than those in existing work. Further, the fidelity evaluation and efficiency comparison reveal that with an average performance improvement of 8.9<inline-formula><tex-math>$% $</tex-math></inline-formula> compared with representative baselines, GAFExplainer achieves the best fidelity metrics while maintaining computational efficiency.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2569-2583"},"PeriodicalIF":8.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769563","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 Security and Privacy in Federated Learning Using Low-Dimensional Update Representation and Proximity-Based Defense","authors":"Wenjie Li;Kai Fan;Jingyuan Zhang;Hui Li;Wei Yang Bryan Lim;Qiang Yang","doi":"10.1109/TKDE.2025.3539717","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3539717","url":null,"abstract":"Federated Learning (FL) is a promising privacy-preserving machine learning paradigm that allows data owners to collaboratively train models while keeping their data localized. Despite its potential, FL faces challenges related to the trustworthiness of both clients and servers, particularly against curious or malicious adversaries. In this paper, we introduce a novel framework named <u>F</u>ederated <u>L</u>earning with Low-Dimensional <u>U</u>pdate <u>R</u>epresentation and <u>P</u>roximity-Based defense (FLURP), designed to address privacy preservation and resistance to Byzantine attacks in distributed learning environments. FLURP employs <inline-formula><tex-math>$mathsf {LinfSample}$</tex-math></inline-formula> method, enabling clients to compute the <inline-formula><tex-math>$l_{infty }$</tex-math></inline-formula> norm across sliding windows of updates, resulting in a Low-Dimensional Update Representation (LUR). Calculating the shared distance matrix among LURs, rather than updates, significantly reduces the overhead of Secure Multi-Party Computation (SMPC) by three orders of magnitude while effectively distinguishing between benign and poisoned updates. Additionally, FLURP integrates a privacy-preserving proximity-based defense mechanism utilizing optimized SMPC protocols to minimize communication rounds. Our experiments demonstrate FLURP's effectiveness in countering Byzantine adversaries with low communication and runtime overhead. FLURP offers a scalable framework for secure and reliable FL in distributed environments, facilitating its application in scenarios requiring robust data management and security.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3372-3385"},"PeriodicalIF":8.9,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896485","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":"Hierarchical Causal Discovery From Large-Scale Observed Variables","authors":"Rujia Shen;Muhan Li;Chao Zhao;Boran Wang;Yi Guan;Jie Liu;Jingchi Jiang","doi":"10.1109/TKDE.2025.3539788","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3539788","url":null,"abstract":"It is a long-standing question to discover causal relations from observed variables in many empirical sciences. However, current causal discovery methods are inefficient when dealing with large-scale observed variables due to challenges in conditional independence (CI) tests or complex computations of acyclicity, and may even fail altogether. To address the efficiency issue in causal discovery from large-scale observed variables, we propose a Hierarchical Causal Discovery (HCD) framework with a bilevel policy that handles this issue by boosting existing models. Specifically, the high-level policy first finds a causal cut set to partition observed variables into several causal clusters and releases the clusters to the low-level policy. The low-level policy applies any causal discovery method to process these causal clusters in parallel and obtain intra-cluster structures for subsequently inter-cluster structure merging in the high-level policy. To avoid missing inter-cluster edges, we theoretically demonstrate the feasibility of causal cluster cut and inter-cluster structure merging. We also prove the completeness and correctness of HCD for causal discovery. Experiments on both synthetic and real-world datasets demonstrate that HCD consistently and significantly enhances the efficiency and effectiveness of existing advanced methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2626-2639"},"PeriodicalIF":8.9,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769552","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":"TaylorS: A Multi-Order Expansion Structure for Urban Spatio-Temporal Forecasting","authors":"Jianyang Qin;Yan Jia;Binxing Fang;Qing Liao","doi":"10.1109/TKDE.2025.3538857","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3538857","url":null,"abstract":"Although a variety of models have been proposed for urban spatio-temporal forecasting, most existing forecasting models are developed manually for specific tasks. By investigating the correlation between multi-order derivative and spatio-temporal data, we propose a generic yet simple plug-in structure, named <bold>TaylorS</b>, to improve the performance and generalization of existing forecasting models. The TaylorS converts the non-linear regression problem into a multi-order non-linear approximation problem by plugging a Taylor expansion into the forecasting task. To achieve this, we design a two-step training framework, including a training step and an adjusting step. During training, we train a given forecasting model as a base model to be equipped with prior knowledge. During adjusting, we fine-tune the base model while plugging an adjustment model into the base model. The adjustment model, as a multi-order expansion, takes the multi-order derivative of data to evaluate data uncertainty for further forecasting approximation and adjustment. Extensive experimental results demonstrate that the proposed TaylorS framework can consistently improve the performance of existing state-of-the-art methods and generalize these methods to different forecasting tasks.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"3030-3046"},"PeriodicalIF":8.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769519","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":"Scalable Multi-View Graph Clustering With Cross-View Corresponding Anchor Alignment","authors":"Siwei Wang;Xinwang Liu;Qing Liao;Yi Wen;En Zhu;Kunlun He","doi":"10.1109/TKDE.2025.3538852","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3538852","url":null,"abstract":"Multi-view graph clustering (MVGC) explores pairwise correlations of entire instances and comprehensively aggregates diverse source information with optimal graph structure. One major issue of practical MVGC is the high time and space complexities prohibiting being applied on large-scale applications. As a promising solution of addressing large-scale problems, anchor-based strategy identifies small portion and key landmarks to serve as replacements for the entire dataset. Despite of its efficiency, anchors chosen across views may be semantically unaligned contrasting to naturally-aligned full sample setting, which may lead to the latter inappropriate graph fusion. Limited attention has been focused on the mentioned Multi-View Anchor-Unaligned Problem (MV-AUP) in the existing literature. In this paper, we first revisit existing multi-view anchor graph clustering frameworks and present the MV-AUP phenomenon. Then, we propose a novel <underline>M</u>ulti-view <underline>C</u>orresponding <underline>A</u>nchor <underline>G</u>raph <underline>A</u>lignment <underline>F</u>usion framework (MV-CAGAF), which elegantly solves MV-AUP with structural representation matching in multi-dimensional spaces. Further, we theoretically prove our proposed structural matching approach can be regarded as minimizing the EMD distance of the two relative anchor distributions. Based on this, we design the innovative multi-view anchor graph fusion paradigm with correspondence alignment, which inherits the linear sample complexity for scalable cross-view clustering. Our proposed MV-CAGAF achieves significant improvements with the help of the novel fusion framework on comprehensive benchmark datasets. Most importantly, the experimental results on both of the simulated and real-world datasets significantly prove the importance of cross-view alignment for large-scale multi-view clustering.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2932-2945"},"PeriodicalIF":8.9,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769367","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":"TagRec: Temporal-Aware Graph Contrastive Learning With Theoretical Augmentation for Sequential Recommendation","authors":"Tianhao Peng;Haitao Yuan;Yongqi Zhang;Yuchen Li;Peihong Dai;Qunbo Wang;Senzhang Wang;Wenjun Wu","doi":"10.1109/TKDE.2025.3538706","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3538706","url":null,"abstract":"Sequential recommendation systems aim to predict the future behaviors of users based on their historical interactions. Despite the success of neural architectures like Transformer and Graph Neural Networks, these models often struggle with the inherent challenge of sparse data in accurately predicting future user behaviors. To alleviate the data sparsity problem, some methods leverage the contrastive learning to generate contrastive views, assuming the items appear discretely at the same time intervals and focusing on the sequence order. However, these approaches neglect the crucial temporal-aware collaborative patterns hidden within the user-item interactions, leading to a limited variety of contrastive pairs and less informative embeddings. The proposed framework, <bold><u>T</u></b>emporal-<bold><u>a</u></b>ware <bold><u>g</u></b>raph contrastive learning with theoretical guarantees for sequential <bold><u>Rec</u></b>ommendation (TagRec), integrates temporal-aware collaborative patterns with adaptive data augmentation to generate more informative user and item representations. TagRec employs a temporal-aware graph neural network to embed the original graph, then generates augmented graphs through the addition of interactions via latent user interest mining, the dropping of redundant interaction edges, and the perturbation of temporal information. Theoretical guarantees are provided that these augmentations enhance the graph’s utility. Extensive experiments on real-world datasets demonstrate the superiority of the proposed approach over the state-of-the-art recommendation methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"3015-3029"},"PeriodicalIF":8.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769538","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":"CogLign: Interpretable Text Sentiment Determination by Aligning Cognition Between EEG-Derived Brain Graph and Text-Derived Knowledge Graph","authors":"Huan Rong;Wenxuan Ji;Tinghuai Ma;Weiyi Ding;Victor S. Sheng","doi":"10.1109/TKDE.2025.3538618","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3538618","url":null,"abstract":"Nowadays, detecting sentiment or emotion from user generated texts has been intensively studied in natural language understanding, especially via neural-based models based on text representation. However, the interpretability on how could the final text sentiment be determined by neural-based text representation has not been thoroughly unfolded yet. Consequently, in this paper, we propose <italic>CogLign</i> which injects the <italic>neural-cognition</i> derived from Electroencephalogram (EEG)-signal into the <italic>neural-based</i> text sentiment analysis model, aimed at learning the activation of brain regions stimulated by different sentiments, so as to guide our proposed <italic>CogLign</i> to make proper determination on text sentiment in brain-like way. Specifically, on the one hand, the given videos in different sentiments have been watched by <italic>subjects</i>, during which the EEG-signals are monitored to construct brain connectivity pattern as <italic>brain graph</i> (<bold>BG</b>), attaining more obvious sentiment response on brain region activation for <italic>neural-cognition</i>. On the other hand, we interpret the video-plots (or video-semantics) along timeline into text, where the entire video-interpreted-text will be <bold>strictly bound</b> with the whole <italic>EEG-signal-sequence</i> by <italic>segment</i> via the fixed size of <italic>time-window</i>. Then, entities and relations are extracted from the video-interpreted-text to construct <italic>knowledge graph</i> (<bold>KG</b>), depicting text semantics. Next, mapping from <italic>entities</i> (or nodes) in <bold>KG</b> to <italic>EEG-Electrodes</i> (or nodes) in <bold>BG</b>, further dated back to different brain regions, has been learned via <italic>cognition alignment</i> between the EEG-derived <bold>BG</b> and text-derived <bold>KG</b>. In this way, by aligning <italic>neural cognition</i> from <italic>brain graph</i> with the <italic>semantic cognition</i> from <italic>knowledge graph</i>, our proposed framework <italic>CogLign</i> can not only achieve the overall best sentiment analysis performance on the <italic>video-interpreted-text</i>, but can also detect brain connectivity patterns in different sentiments more consistent with the prior conclusion of brain region sentiment preference, revealing competitive <italic>interpretability</i> on text sentiment determination.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3220-3239"},"PeriodicalIF":8.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896272","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":"Towards Stable and Explainable Attention Mechanisms","authors":"Lijie Hu;Xinhai Wang;Yixin Liu;Ninghao Liu;Mengdi Huai;Lichao Sun;Di Wang","doi":"10.1109/TKDE.2025.3538583","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3538583","url":null,"abstract":"Currently, attention mechanism has become a standard fixture in most state-of-the-art natural language processing (NLP) models, not only due to the outstanding performance it could gain but also due to plausible innate explanations for the behaviors of neural architectures it provides, which is notoriously difficult to analyze. However, recent studies show that attention is unstable against randomness and perturbations during training or testing, such as random seeds and slight perturbation of embedding vectors, which impedes it from becoming a faithful explanation tool. Thus, a natural question is whether we can find some substitute for the current attention that is more stable and could keep the most important characteristics of explanation and prediction of attention. In this paper, to resolve the problem, we provide a rigorous definition of such alternate namely SEAT (<u><b>S</b></u>table and <u><b>E</b></u>xplainable <u><b>At</b></u>tention). Specifically, a SEAT should have the following three properties: (1) Its prediction distribution is enforced to be close to the distribution based on the vanilla attention; (2) Its top-<inline-formula><tex-math>$k$</tex-math></inline-formula> indices have large overlaps with those of the vanilla attention; (3) It is robust w.r.t perturbations, i.e., any slight perturbation on SEAT will not change the prediction distribution too much, which implicitly indicates that it is stable to randomness and perturbations. To further improve the interpretability stability against perturbations, based on SEAT we provide another definition called SEAT++. Then we propose a method to get a SEAT++, which could be considered an ad hoc modification for canonical attention. Finally, through intensive experiments on various datasets, we compare our SEAT and SEAT++ with other baseline methods using RNN, BiLSTM, and BERT architectures via six different evaluation metrics for model interpretation, stability, and accuracy. Results show that SEAT and SEAT++ are more stable against different perturbations and randomness while also keeping the explainability of attention, which indicates they provide more faithful explanations. Moreover, compared with vanilla attention, there is almost no utility (accuracy) degradation for SEAT and SEAT++.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"3047-3061"},"PeriodicalIF":8.9,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769564","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}