IEEE Transactions on Knowledge and Data Engineering最新文献_第9页

SCHENO: Measuring Schema vs. Noise in Graphs SCHENO：测量图式与图中的噪声

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-17 DOI: 10.1109/TKDE.2025.3543032

Justus Isaiah Hibshman;Adnan Hoq;Tim Weninger

引用次数: 0

Spatio-Temporal Multivariate Probabilistic Modeling for Traffic Prediction 交通预测的时空多元概率模型

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-14 DOI: 10.1109/TKDE.2025.3539680

Yang An;Zhibin Li;Xiaoyu Li;Wei Liu;Xinghao Yang;Haoliang Sun;Meng Chen;Yu Zheng;Yongshun Gong

{"title":"Spatio-Temporal Multivariate Probabilistic Modeling for Traffic Prediction","authors":"Yang An;Zhibin Li;Xiaoyu Li;Wei Liu;Xinghao Yang;Haoliang Sun;Meng Chen;Yu Zheng;Yongshun Gong","doi":"10.1109/TKDE.2025.3539680","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3539680","url":null,"abstract":"Traffic prediction is an essential task in intelligent transportation systems dealing with complex and dynamic spatio-temporal correlations. To date, most work is focused on point estimation models, which only output a single value w.r.t an attribute of traffic data at a time, falling short of depicting diverse situations and uncertainty in future. Besides, most methods are not flexible enough to handle real complex traffic scenarios, involving missing values and non-uniformly sampled data. The interactions among different attributes of traffic data are also rarely explored explicitly. In this paper, we focus on probabilistic estimation in traffic prediction tasks, proposing a spatio-temporal multivariate probabilistic predictive model to estimate the distributions of traffic data. Specifically, we devise a multivariate spatio-temporal fusion graph block to extract spatio-temporal correlations of multiple traffic attributes at different locations. A multi-graph fusion module is designed to capture time-varying spatial relationships. We estimate the joint distributions of missing traffic data using copulas. The proposed model can simultaneously perform traffic forecasting and interpolation tasks with non-uniformly sampled data. Our experiments on two real-world traffic datasets demonstrate the advantages of our model over the state-of-the-art1.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2986-3000"},"PeriodicalIF":8.9,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769556","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}

引用次数: 0

Causal-TSF: A Causal Intervention Approach to Mitigate Confounding Bias in Time Series Forecasting 因果- tsf：一种减轻时间序列预测混杂偏差的因果干预方法

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-14 DOI: 10.1109/TKDE.2025.3536107

Qinkang Gong;Yan Pan;Hanjiang Lai;Rongbang Qiu;Jian Yin

{"title":"Causal-TSF: A Causal Intervention Approach to Mitigate Confounding Bias in Time Series Forecasting","authors":"Qinkang Gong;Yan Pan;Hanjiang Lai;Rongbang Qiu;Jian Yin","doi":"10.1109/TKDE.2025.3536107","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3536107","url":null,"abstract":"Time series forecasting, aiming to learn models from historical data and predict future values in time series, is a fundamental research topic in machine learning. However, few efforts have been devoted to addressing the confounding effects in time series data, e.g., the historical data are affected by some hidden surrounding factors (i.e., confounders), leading to biased forecasting models for future data. This paper presents a causal intervention approach to eliminate the bias that is raised by some hidden confounders. By using a causal graph, we illustrate why hidden confounders can bring bias in time series forecasting and how to tackle it. We implement causal intervention by a deep architecture that consists of two modules, a Confounders Estimation module to estimate the hidden confounders and a Debiasing module to eliminate the confounding bias in the forecasting model via sampling on confounders. We conduct comprehensive evaluations on various time series datasets. The experiment results indicate that the proposed method can reduce the negative confounding effects in time series data, and it achieves superior gains over state-of-the-art baselines for time series forecasting.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3205-3219"},"PeriodicalIF":8.9,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896459","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}

引用次数: 0

Learning Location-Guided Time-Series Shapelets 学习位置引导的时间序列Shapelets

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-13 DOI: 10.1109/TKDE.2025.3536462

Akihiro Yamaguchi;Ken Ueno;Hisashi Kashima

{"title":"Learning Location-Guided Time-Series Shapelets","authors":"Akihiro Yamaguchi;Ken Ueno;Hisashi Kashima","doi":"10.1109/TKDE.2025.3536462","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3536462","url":null,"abstract":"Shapelets are interclass discriminative subsequences that can be used to characterize target classes. Learning shapelets by continuous optimization has recently been studied to improve classification accuracy. However, there are two issues in previous studies. First, since the locations where shapelets appear in the time series are determined by only their shapes, shapelets may appear at incorrect and non-discriminative locations in the time series, degrading the accuracy and interpretability. Second, the theoretical interpretation of learned shapelets has been limited to binary classification. To tackle the first issue, we propose a continuous optimization that learns not only shapelets but also their probable locations in a time series, and we show theoretically that this enhances feature discriminability. To tackle the second issue, we provide a theoretical interpretation of shapelet closeness to the time series for target / off-target classes when learning with softmax loss, which allows for multi-class classification. We demonstrate the effectiveness of the proposed method in terms of accuracy, runtime, and interpretability on the UCR archive.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2712-2726"},"PeriodicalIF":8.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769456","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}

引用次数: 0

RAGIC: Risk-Aware Generative Framework for Stock Interval Construction 股票区间构建的风险感知生成框架

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-13 DOI: 10.1109/TKDE.2025.3533492

Jingyi Gu;Wenlu Du;Guiling Wang

{"title":"RAGIC: Risk-Aware Generative Framework for Stock Interval Construction","authors":"Jingyi Gu;Wenlu Du;Guiling Wang","doi":"10.1109/TKDE.2025.3533492","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3533492","url":null,"abstract":"Efforts to predict stock market outcomes have yielded limited success due to the inherently stochastic nature of the market, influenced by numerous unpredictable factors. Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making and often overlooking market risk. To bridge this gap, we propose <italic>RAGIC, a novel risk-aware framework for stock <italic>interval prediction to quantify uncertainty. Our approach leverages a Generative Adversarial Network (GAN) to produce future price sequences infused with randomness inherent in financial markets. <italic>RAGIC’s generator detects the risk perception of informed investors and captures historical price trends globally and locally. Then the <italic>risk-sensitive intervals is built upon the simulated future prices from sequence generation through statistical inference, incorporating <italic>horizon-wise insights. The interval’s width is adaptively adjusted to reflect market volatility. Importantly, our approach relies solely on publicly available data and incurs only low computational overhead. <italic>RAGIC’s evaluation across globally recognized broad-based indices demonstrates its balanced performance, offering both accuracy and informativeness. Achieving a consistent 95% coverage, <italic>RAGIC maintains a narrow interval width. This promising outcome suggests that our approach effectively addresses the challenges of stock market prediction while incorporating vital risk considerations.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"2085-2096"},"PeriodicalIF":8.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570797","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}

引用次数: 0

Multi-View Riemannian Manifolds Fusion Enhancement for Knowledge Graph Completion 知识图补全的多视图黎曼流形融合增强

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-13 DOI: 10.1109/TKDE.2025.3538110

Linyu Li;Zhi Jin;Xuan Zhang;Haoran Duan;Jishu Wang;Zhengwei Tao;Haiyan Zhao;Xiaofeng Zhu

{"title":"Multi-View Riemannian Manifolds Fusion Enhancement for Knowledge Graph Completion","authors":"Linyu Li;Zhi Jin;Xuan Zhang;Haoran Duan;Jishu Wang;Zhengwei Tao;Haiyan Zhao;Xiaofeng Zhu","doi":"10.1109/TKDE.2025.3538110","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3538110","url":null,"abstract":"As the application of knowledge graphs becomes increasingly widespread, the issue of knowledge graph incompleteness has garnered significant attention. As a classical type of non-euclidean spatial data, knowledge graphs possess various complex structural types. However, most current knowledge graph completion models are developed within a single space, which makes it challenging to capture the inherent knowledge information embedded in the entire knowledge graph. This limitation hinders the representation learning capability of the models. To address this issue, this paper focuses on how to better extend the representation learning from a single space to Riemannian manifolds, which are capable of representing more complex structures. We propose a new knowledge graph completion model called MRME-KGC, based on multi-view Riemannian Manifolds fusion to achieve this. Specifically, MRME-KGC simultaneously considers the fusion of four views: two hyperbolic Riemannian spaces with negative curvature, a Euclidean Riemannian space with zero curvature, and a spherical Riemannian space with positive curvature to enhance knowledge graph modeling. Additionally, this paper proposes a contrastive learning method for Riemannian spaces to mitigate the noise and representation issues arising from Multi-view Riemannian Manifolds Fusion. This paper presents extensive experiments on MRME-KGC across multiple datasets. The results consistently demonstrate that MRME-KGC significantly outperforms current state-of-the-art models, achieving highly competitive performance even with low-dimensional embeddings.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2756-2770"},"PeriodicalIF":8.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769363","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}

引用次数: 0

Finding Rule-Interpretable Non-Negative Data Representation 寻找可规则解释的非负数据表示

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-13 DOI: 10.1109/TKDE.2025.3538327

Matej Mihelčić;Pauli Miettinen

{"title":"Finding Rule-Interpretable Non-Negative Data Representation","authors":"Matej Mihelčić;Pauli Miettinen","doi":"10.1109/TKDE.2025.3538327","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3538327","url":null,"abstract":"Non-negative Matrix Factorization (NMF) is an intensively used technique for obtaining parts-based, lower dimensional and non-negative representation. Researchers in biology, medicine, pharmacy and other fields often prefer NMF over other dimensionality reduction approaches (such as PCA) because the non-negativity of the approach naturally fits the characteristics of the domain problem and its results are easier to analyze and understand. Despite these advantages, obtaining exact characterization and interpretation of the NMF’s latent factors can still be difficult due to their numerical nature. Rule-based approaches, such as rule mining, conceptual clustering, subgroup discovery and redescription mining, are often considered more interpretable but lack lower-dimensional representation of the data. We present a version of the NMF approach that merges rule-based descriptions with advantages of part-based representation offered by the NMF. Given the numerical input data with non-negative entries and a set of rules with high entity coverage, the approach creates the lower-dimensional non-negative representation of the input data in such a way that its factors are described by the appropriate subset of the input rules. In addition to revealing important attributes for latent factors, their interaction and value ranges, this approach allows performing focused embedding potentially using multiple overlapping target labels.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2538-2549"},"PeriodicalIF":8.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10887020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777910","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}

引用次数: 0

Dual-State Personalized Knowledge Tracing With Emotional Incorporation 基于情感整合的双状态个性化知识追踪

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-13 DOI: 10.1109/TKDE.2025.3538121

Shanshan Wang;Fangzheng Yuan;Keyang Wang;Xun Yang;Xingyi Zhang;Meng Wang

{"title":"Dual-State Personalized Knowledge Tracing With Emotional Incorporation","authors":"Shanshan Wang;Fangzheng Yuan;Keyang Wang;Xun Yang;Xingyi Zhang;Meng Wang","doi":"10.1109/TKDE.2025.3538121","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3538121","url":null,"abstract":"Knowledge tracing has been widely used in online learning systems to guide the students’ future learning. However, most existing KT models primarily focus on extracting abundant information from the question sets and explore the relationships between them, but ignore the personalized student behavioral information in the learning process. This will limit the model’s ability to accurately capture the personalized knowledge states of students and reasonably predict their performances. To alleviate this limitation, we explicitly models the personalized learning process by incorporating the emotions, a representative personalized behavior in the learning process, into KT framework. Specifically, we present a novel Dual-State Personalized Knowledge Tracing with Emotional Incorporation model to achieve this goal: First, we incorporate emotional information into the modeling process of knowledge state, resulting in the Knowledge State Boosting Module. Second, we design an Emotional State Tracing Module to monitor students’ personalized emotional states, and propose an emotion prediction method based on personalized emotional states. Finally, we apply the predicted emotions to enhance students’ response prediction. Furthermore, to extend the generalization capability of our model across different datasets, we design a transferred version of DEKT, named Transfer Learning-based Self-loop model (T-DEKT). Extensive experiments show our method achieves the state-of-the-art performance.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2440-2455"},"PeriodicalIF":8.9,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769365","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}

引用次数: 0

CoLLM: Integrating Collaborative Embeddings Into Large Language Models for Recommendation 将协作嵌入集成到推荐的大型语言模型中

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-12 DOI: 10.1109/TKDE.2025.3540912

Yang Zhang;Fuli Feng;Jizhi Zhang;Keqin Bao;Qifan Wang;Xiangnan He

{"title":"CoLLM: Integrating Collaborative Embeddings Into Large Language Models for Recommendation","authors":"Yang Zhang;Fuli Feng;Jizhi Zhang;Keqin Bao;Qifan Wang;Xiangnan He","doi":"10.1109/TKDE.2025.3540912","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3540912","url":null,"abstract":"Leveraging Large Language Models as recommenders, referred to as LLMRec, is gaining traction and brings novel dynamics for modeling user preferences, particularly for cold-start users. However, existing LLMRec approaches primarily focus on text semantics and overlook the crucial aspect of incorporating collaborative information from user-item interactions, leading to potentially sub-optimal performance in warm-start scenarios. To ensure superior recommendations across both warm and cold scenarios, we introduce <italic>CoLLM, an innovative LLMRec approach that explicitly integrates collaborative information for recommendations. CoLLM treats collaborative information as a distinct modality, directly encoding it from well-established traditional collaborative models, and then tunes a mapping module to align this collaborative information with the LLM's input text token space for recommendations. By externally integrating traditional models, CoLLM ensures effective collaborative information modeling without modifying the LLM itself, providing the flexibility to adopt diverse collaborative information modeling mechanisms. Extensive experimentation validates that CoLLM adeptly integrates collaborative information into LLMs, resulting in enhanced recommendation performance.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2329-2340"},"PeriodicalIF":8.9,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769452","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}

引用次数: 0

Acceleration Algorithms in GNNs: A Survey GNNs中的加速算法综述

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-11 DOI: 10.1109/TKDE.2025.3540787

Lu Ma;Zeang Sheng;Xunkai Li;Xinyi Gao;Zhezheng Hao;Ling Yang;Xiaonan Nie;Jiawei Jiang;Wentao Zhang;Bin Cui

引用次数: 0