IEEE Transactions on Knowledge and Data Engineering最新文献_第6页

GSM-EL: A Generalizable Symbol-Manipulation Approach for Entity Linking

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-12-27 DOI: 10.1109/TKDE.2024.3523399

Xueqi Cheng;Yuanzheng Wang;Yixing Fan;Jiafeng Guo;Ruqing Zhang;Keping Bi

引用次数: 0

MimoSketch: A Framework for Frequency-Based Mining Tasks on Multiple Nodes With Sketches

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-12-26 DOI: 10.1109/TKDE.2024.3523034

Wenfei Wu;Yuchen Xu

{"title":"MimoSketch: A Framework for Frequency-Based Mining Tasks on Multiple Nodes With Sketches","authors":"Wenfei Wu;Yuchen Xu","doi":"10.1109/TKDE.2024.3523034","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3523034","url":null,"abstract":"In distributed data stream mining, we abstract a MIMO scenario where a stream of <underline>multiple <underline>items is mined by <underline>multiple n<underline>odes. We design a framework named MimoSketch for the MIMO-specific scenario, which improves the fundamental mining tasks of item frequency estimation, item size distribution estimation, heavy hitter detection, heavy change detection, and entropy estimation. MimoSketch consists of an algorithm design and a policy to schedule items to nodes. MimoSketch's algorithm applies random counting to preserve a mathematically proven <italic>unbiasedness property, which makes it friendly to the aggregate query on multiple nodes; its memory layout is <italic>dynamically adaptive to the runtime item size distribution, which maximizes the estimation accuracy by storing more items. MimoSketch's scheduling policy balances items among nodes, avoiding nodes being overloaded or underloaded, which improves the overall mining accuracy. Our prototype and evaluation show that our algorithm can improve the accuracy of five typical mining tasks by an order of magnitude compared with the state-of-the-art solutions, and the scheduling policy further promotes the performance in MIMO scenarios.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1311-1324"},"PeriodicalIF":8.9,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106902","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

Segmented Sequence Prediction Using Variable-Order Markov Model Ensemble

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-12-26 DOI: 10.1109/TKDE.2024.3522975

Weichao Yan;Hao Ma;Zaiyue Yang

{"title":"Segmented Sequence Prediction Using Variable-Order Markov Model Ensemble","authors":"Weichao Yan;Hao Ma;Zaiyue Yang","doi":"10.1109/TKDE.2024.3522975","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3522975","url":null,"abstract":"In recent years, sequence prediction, particularly in natural language processing tasks, has made significant progress due to advanced neural network architectures like Transformer and enhanced computing power. However, challenges persist in modeling and analyzing certain types of sequence data, such as human daily activities and competitive ball games. These segmented sequence data are characterized by short length, varying local dependencies, and coarse-grained unit states. These characteristics limit the effectiveness of conventional probabilistic graphical models and attention-based or recurrent neural networks in modeling and analyzing segmented sequence data. To address this gap, we introduce a novel generative model for segmented sequences, employing an ensemble of multiple variable-order Markov models (VOMMs) to flexibly represent state transition dependencies. Our approach integrates probabilistic graphical models with neural networks, surpassing the representation capabilities of single high-order or variable-order Markov models. Compared to end-to-end deep learning models, our method offers improved interpretability and reduces overfitting in short segments. We demonstrate the efficacy of our proposed method in two tasks: predicting tennis shot types and forecasting daily action sequences. These applications highlight the broad applicability of our segmented sequence modeling approach across diverse domains.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1425-1438"},"PeriodicalIF":8.9,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106842","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-Behavior Hypergraph Contrastive Learning for Session-Based Recommendation

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-12-26 DOI: 10.1109/TKDE.2024.3523383

Liangmin Guo;Shiming Zhou;Haiyue Tang;Xiaoyao Zheng;Yonglong Luo

{"title":"Multi-Behavior Hypergraph Contrastive Learning for Session-Based Recommendation","authors":"Liangmin Guo;Shiming Zhou;Haiyue Tang;Xiaoyao Zheng;Yonglong Luo","doi":"10.1109/TKDE.2024.3523383","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3523383","url":null,"abstract":"Most current session-based recommendations model session sequences solely based on the user's target behavior, ignoring the user's hidden preferences in auxiliary behaviors. Additionally, they use ordinary graphs to model one-to-one item correlations in the current session and fail to leverage other sessions to learn richer higher-order item correlations. To address these issues, a multi-behavior hypergraph contrastive learning model for session-based recommendations is proposed. This model represents all the sessions as global hypergraphs according to two types of behavior sequences. It employs contrastive learning to obtain global item embeddings, which are further aggregated to generate a global session representation that captures higher-order correlations of items from all session perspectives. A novel local heterogeneous hypergraph is designed for the current session to capture higher-order correlations between items with different behaviors in the current session, thus enhancing the local session representation. Additionally, a novel self-supervised signal is created by constructing a multi-behavior line graph, enhancing the global session representation. Finally, the local session representation, global session representation, and global item embedding are used to learn the predicted interaction probability of each item. Extensive experiments are conducted on three real datasets, and the results demonstrate that the proposed model significantly improves recommendation accuracy.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1325-1338"},"PeriodicalIF":8.9,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106897","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

Hyperbolic Graph Contrastive Learning for Collaborative Filtering

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-12-26 DOI: 10.1109/TKDE.2024.3522960

Zhida Qin;Wentao Cheng;Wenxing Ding;Gangyi Ding

{"title":"Hyperbolic Graph Contrastive Learning for Collaborative Filtering","authors":"Zhida Qin;Wentao Cheng;Wenxing Ding;Gangyi Ding","doi":"10.1109/TKDE.2024.3522960","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3522960","url":null,"abstract":"Hyperbolic space based collaborative filtering has emerged as a popular topic in recommender systems. Compared to the euclidean space, hyperbolic space is more suitable to the tree-like structures in the user-item interactions and can achieve better recommender performance. Although some works have been devoted to this popular topic and made some progresses, they use tangent space as an approximation of hyperbolic space to implement model. Despite the effectiveness, such methods fail to fully exploit the advantages of hyperbolic space and still suffer from the data sparsity issue, which severely limits the recommender performance. To tackle these problems, we refer to the self-supervised learning technique and novelly propose a <bold>Hyperbolic Graph <bold>Contrastive <bold>Learning (<italic>HyperCL) framework. Specifically, our framework encodes the augmentation views from both the tangent space and the hyperbolic space, and construct the contrast pairs based on their corresponding learned node representations. Our model not only leverages the geometric advantages of both sides but also achieves seamless information transmission between the two spaces. Extensive experimental results on public benchmark datasets demonstrate that our model is highly competitive and outperforms leading baselines by considerable margins. Further experiments validate the robustness and the superiority of contrastive learning paradigm.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1255-1267"},"PeriodicalIF":8.9,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106809","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

FRAME: Feature Rectification for Class Imbalance Learning

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-12-26 DOI: 10.1109/TKDE.2024.3523043

Xu Cheng;Fan Shi;Yao Zhang;Huan Li;Xiufeng Liu;Shengyong Chen

{"title":"FRAME: Feature Rectification for Class Imbalance Learning","authors":"Xu Cheng;Fan Shi;Yao Zhang;Huan Li;Xiufeng Liu;Shengyong Chen","doi":"10.1109/TKDE.2024.3523043","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3523043","url":null,"abstract":"Class imbalance learning is a challenging task in machine learning applications. To balance training data, traditional class imbalance learning approaches, such as class resampling or reweighting, are commonly applied in the literature. However, these methods can have significant limitations, particularly in the presence of noisy data, missing values, or when applied to advanced learning paradigms like semi-supervised or federated learning. To address these limitations, this paper proposes a novel and theoretically-ensured latent <bold>Feature <bold>Rectification method for cl<bold>Ass i<bold>Mbalance l<bold>Earning (FRAME). The proposed FRAME can automatically learn multiple centroids for each class in the latent space and then perform class balancing. Unlike data-level methods, FRAME balances feature in the latent space rather than the original space. Compared to algorithm-level methods, FRAME can distinguish different classes based on distance without the need to adjust the learning algorithms. Through latent feature rectification, FRAME can effectively mitigate contaminated noises/missing values without worrying about structural variations in the data. In order to accommodate a wider range of applications, this paper extends FRAME to the following three main learning paradigms: fully-supervised learning, semi-supervised learning, and federated learning. Extensive experiments on 10 binary-class datasets demonstrate that our FRAME can achieve competitive performance than the state-of-the-art methods and its robustness to noises/missing values.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1167-1181"},"PeriodicalIF":8.9,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106883","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

Achieving Top-$K$K-fairness for Finding Global Top-$K$K Frequent Items

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-12-26 DOI: 10.1109/TKDE.2024.3523033

Yikai Zhao;Wei Zhou;Wenchen Han;Zheng Zhong;Yinda Zhang;Xiuqi Zheng;Tong Yang;Bin Cui

{"title":"Achieving Top-$K$K-fairness for Finding Global Top-$K$K Frequent Items","authors":"Yikai Zhao;Wei Zhou;Wenchen Han;Zheng Zhong;Yinda Zhang;Xiuqi Zheng;Tong Yang;Bin Cui","doi":"10.1109/TKDE.2024.3523033","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3523033","url":null,"abstract":"Finding top-<inline-formula><tex-math>$K$</tex-math></inline-formula> frequent items has been a hot topic in data stream processing with wide-ranging applications. However, most existing sketch algorithms focus on finding local top-<inline-formula><tex-math>$K$</tex-math></inline-formula> in a single data stream. In this paper, we tackle finding global top-<inline-formula><tex-math>$K$</tex-math></inline-formula> across multiple data streams. We find that using prior sketch algorithms directly is often unfair in global scenarios, degrading global top-<inline-formula><tex-math>$K$</tex-math></inline-formula> accuracy. We define top-<inline-formula><tex-math>$K$</tex-math></inline-formula>-fairness and show its importance for finding global top-<inline-formula><tex-math>$K$</tex-math></inline-formula>. To achieve this, we propose the Double-Anonymous (DA) sketch, where double-anonymity ensures fairness. We also propose two techniques, hot-filtering and early-freezing, to improve accuracy further. We theoretically prove that the DA sketch achieves top-<inline-formula><tex-math>$K$</tex-math></inline-formula>-fairness while maintaining high accuracy. Extensive experiments verify top-<inline-formula><tex-math>$K$</tex-math></inline-formula>-fairness in disjoint data streams, showing that the DA sketch's error is up to 129 times (60 times on average) smaller than the state-of-the-art. To enhance the applicability and technical depth, we also investigate how to extend the DA sketch to general distributed data stream scenarios and how to provide a fairer and more accurate global ranking for top-<inline-formula><tex-math>$K$</tex-math></inline-formula> items. The experimental results show that the extended version of the DA sketch can indeed compute better rankings and still has significant advantages in general data streams.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1508-1526"},"PeriodicalIF":8.9,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570611","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

Nowhere to H2IDE: Fraud Detection From Multi-Relation Graphs via Disentangled Homophily and Heterophily Identification

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-12-26 DOI: 10.1109/TKDE.2024.3523107

Chao Fu;Guannan Liu;Kun Yuan;Junjie Wu

{"title":"Nowhere to H2IDE: Fraud Detection From Multi-Relation Graphs via Disentangled Homophily and Heterophily Identification","authors":"Chao Fu;Guannan Liu;Kun Yuan;Junjie Wu","doi":"10.1109/TKDE.2024.3523107","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3523107","url":null,"abstract":"Fraud detection has always been one of the primary concerns in social and economic activities and is becoming a decisive force in the booming digital economy. Graph structures formed by rich user interactions naturally serve as important clues for identifying fraudsters. While numerous graph neural network-based methods have been proposed, the diverse interactive connections within graphs and the heterophilic connections deliberately established by fraudsters to normal users as camouflage pose new research challenges. In this light, we propose H2IDE (Homophily and Heterophily Identification with Disentangled Embeddings) for accurate fraud detection in multi-relation graphs. H2IDE features in an independence-constrained disentangled representation learning scheme to capture various latent behavioral patterns in graphs, along with a supervised identification task to specifically model the factor-wise heterophilic connections, both of which are proven crucial to fraud detection. We also design a relation-aware attention mechanism for hierarchical and adaptive neighborhood aggregation in H2IDE. Extensive comparative experiments with state-of-the-art baseline methods on two real-world multi-relation graphs and two large-scale homogeneous graphs demonstrate the superiority and scalability of our proposed method and highlight the key role of disentangled representation learning with homophily and heterophily identification.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1380-1393"},"PeriodicalIF":8.9,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106887","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

Conversational Recommendations With User Entity Focus and Multi-Granularity Latent Variable Enhancement

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-12-26 DOI: 10.1109/TKDE.2024.3523283

Yunfei Yin;Yiming Pan;Xianjian Bao;Faliang Huang

{"title":"Conversational Recommendations With User Entity Focus and Multi-Granularity Latent Variable Enhancement","authors":"Yunfei Yin;Yiming Pan;Xianjian Bao;Faliang Huang","doi":"10.1109/TKDE.2024.3523283","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3523283","url":null,"abstract":"Conversational recommendation is one system that can extract the user's preferences and recommend suitable items in a similar way to human-like responses. Existing methods often use the feature extraction combined with the Transformer model to extract user preferences and make recommendations. However, these methods have two limitations. First, they do not consider the order in which entities appear, thus affecting the extraction of user preferences. Second, the generated responses lack diversity that affects the users’ experience to the system. To this end, we propose a conversational recommendation model with User Entity focus and Multi-Granularity latent variable enhancement (UEMG). In UEMG, we design a novel neural network that utilizes Bi-GRU to capture the appearing orders of entities in dialogues, and leverages Transformer to capture the global dependencies of entities, and then combines them to extract user preferences. For the second issue, to improve the diversity of dialogue generation, we propose a multi-granularity latent variable mechanism, which can extract more entities from the context information and the knowledge graphs, respectively. We conducted extensive experiments on publicly available dialogue generation datasets. Experimental results demonstrate that compared to current state-of-the-art methods, UEMG achieves 9.7% improvements in recommendation performance and 23% improvements in dialogue generation.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1126-1139"},"PeriodicalIF":8.9,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106835","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

Self-Correcting Clustering

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2024-12-26 DOI: 10.1109/TKDE.2024.3523021

Hanxuan Wang;Na Lu;Zixuan Wang;Yuxuan Yan;Gustavo Carneiro;Zhen Wang

{"title":"Self-Correcting Clustering","authors":"Hanxuan Wang;Na Lu;Zixuan Wang;Yuxuan Yan;Gustavo Carneiro;Zhen Wang","doi":"10.1109/TKDE.2024.3523021","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3523021","url":null,"abstract":"The incorporation of target distribution significantly enhances the success of deep clustering. However, most of the related deep clustering methods suffer from two drawbacks: (1) manually-designed target distribution functions with uncertain performance and (2) cluster misassignment accumulation. To address these issues, a <underline>Self-<underline>Correcting <underline>Clustering (Self-CC) framework is proposed. In Self-CC, a robust target distribution solver (RTDS) is designed to automatically predict the target distribution and alleviate the adverse influence of misassignments. Specifically, RTDS divides the high confidence samples selected according to the cluster assignments predicted by a clustering module into labeled samples with correct pseudo labels and unlabeled samples of possible misassignments by modeling its training loss distribution. With the divided data, RTDS can be trained in a semi-supervised way. The critical hyperparameter which controls the semi-supervised training process can be set adaptively by estimating the distribution property of misassignments in the pseudo-label space with the support of a theoretical analysis. The target distribution can be predicted by the well-trained RTDS automatically, optimizing the clustering module and correcting misassignments in the cluster assignments. The clustering module and RTDS mutually promote each other forming a positive feedback loop. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the proposed Self-CC.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1439-1454"},"PeriodicalIF":8.9,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106840","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