IEEE Transactions on Knowledge and Data Engineering最新文献_第7页

Topic Videolization: A Rumor Detection Method Inspired by Video Forgery Detection Technology 话题视频化：一种受视频伪造检测技术启发的谣言检测方法

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-20 DOI: 10.1109/TKDE.2025.3543852

Yucai Pang;Zhou Yang;Qian Li;Shihong Wei;Yunpeng Xiao

{"title":"Topic Videolization: A Rumor Detection Method Inspired by Video Forgery Detection Technology","authors":"Yucai Pang;Zhou Yang;Qian Li;Shihong Wei;Yunpeng Xiao","doi":"10.1109/TKDE.2025.3543852","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543852","url":null,"abstract":"This study was inspired by video forgery detection techniques. If the topic space at a certain time is considered as a frame image, the consecutive frame images over time could be viewed as a video. Then the rumor topic detection problem is transformed into a topic video forgery detection problem. Thus, a novel rumor detection method was proposed. First, a Topic2RGB algorithm was proposed to convert comment users into pixel points. The algorithm views commenting users as pixel points while using game theory to mine user pro-opposition emotions as RGB information. Secondly, a Topic2Video algorithm was proposed to convert the topic space into video. The algorithm converts the topic space into frame images. Meanwhile, the topic space is time-sliced, then the topic space is transformed into a video. Finally, the volatility of user emotional confrontation during a long time in the topic space is like the change of characteristics of frame images in forgeries videos. Then, a topic video rumor detection method (TVRD) was proposed. The experiments indicate that the method successfully verifies the viability of the topic videolization for rumor detection. Additionally, the method also demonstrates the effectiveness of user emotion confrontation of topic space on detection performance.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3753-3765"},"PeriodicalIF":8.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896483","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

Enhancing Attribute-Driven Fraud Detection With Risk-Aware Graph Representation 基于风险感知图表示的属性驱动欺诈检测

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-20 DOI: 10.1109/TKDE.2025.3543887

Sheng Xiang;Guibin Zhang;Dawei Cheng;Ying Zhang

{"title":"Enhancing Attribute-Driven Fraud Detection With Risk-Aware Graph Representation","authors":"Sheng Xiang;Guibin Zhang;Dawei Cheng;Ying Zhang","doi":"10.1109/TKDE.2025.3543887","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543887","url":null,"abstract":"Credit card fraud is a severe issue that causes significant losses for both cardholders and issuing banks. Existing methods utilize machine learning-based classifiers to identify fraudulent transactions from labeled transaction records. However, labeled data are often scarce compared to the billions of real transactions due to the high cost of annotation, which means that previous methods do not fully utilize the rich features of unlabeled data. What’s more, contemporary methods succumb to a fallacy of unawareness of the local risk structure and the inability to capture certain risk patterns. Therefore, we propose the Risk-aware Gated Temporal Attention Network (RGTAN) for fraud detection in this work. Specifically, we first build a temporal transaction graph based on the transaction records, which consists of temporal transactions (nodes) and their interactions (edges). Then we leverage a Gated Temporal Graph Attention (GTGA) Mechanism to propagate messages among the nodes and learn adaptive representations of transactions. We also model the fraud patterns through risk propagation, taking advantage of the relations among transactions. More importantly, we devise a neighbor risk-aware representation learning layer to enhance our method’s perception of multi-hop risk structures. We conduct extensive experiments on a real-world credit card transaction dataset and two public fraud detection datasets. The results show that our proposed method, RGTAN, outperforms other state-of-the-art methods on three fraud detection datasets. The risk-aware semi-supervised experiments also demonstrate the excellent performance of our model with only a small fraction of manually labeled data. Moreover, RGTAN has been deployed in a world-leading credit card issuer for credit card fraud detection, and the case study results show the effectiveness of our method in uncovering real-world fraud patterns.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2501-2512"},"PeriodicalIF":8.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769368","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

-Graph: A Graph Embedding for Interpretable Time Series Clustering 图：用于可解释时间序列聚类的图嵌入

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-20 DOI: 10.1109/TKDE.2025.3543946

Paul Boniol;Donato Tiano;Angela Bonifati;Themis Palpanas

引用次数: 0

Scalable Min-Max Multi-View Spectral Clustering 可扩展的最小-最大多视图光谱聚类

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-20 DOI: 10.1109/TKDE.2025.3543817

Ben Yang;Xuetao Zhang;Jinghan Wu;Feiping Nie;Fei Wang;Badong Chen

{"title":"Scalable Min-Max Multi-View Spectral Clustering","authors":"Ben Yang;Xuetao Zhang;Jinghan Wu;Feiping Nie;Fei Wang;Badong Chen","doi":"10.1109/TKDE.2025.3543817","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543817","url":null,"abstract":"Multi-view spectral clustering has attracted considerable attention since it can explore common geometric structures from diverse views. Nevertheless, existing min-min framework-based models adopt internal minimization to find the view combination with the minimized within-cluster variance, which will lead to effectiveness loss since the real clusters often exhibit high within-cluster variance. To address this issue, we provide a novel scalable min-max multi-view spectral clustering (SMMSC) model to improve clustering performance. Besides, anchor graphs, rather than full sample graphs, are utilized to reduce the computational complexity of graph construction and singular value decomposition, thereby enhancing the applicability of SMMSC to large-scale applications. Then, we rewrite the min-max model as a minimized optimal value function, demonstrate its differentiability, and develop an efficient gradient descent-based algorithm to optimize it with linear computational complexity. Moreover, we demonstrate that the resultant solution of the proposed algorithm is the global optimum. Numerous experiments on different real-world datasets, including some large-scale datasets, demonstrate that SMMSC outperforms existing state-of-the-art multi-view clustering methods regarding clustering performance.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2918-2931"},"PeriodicalIF":8.9,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769539","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

Probabilistic Learning of Multivariate Time Series With Temporal Irregularity 具有时间不规则性的多元时间序列的概率学习

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-20 DOI: 10.1109/TKDE.2025.3544348

Yijun Li;Cheuk Hang Leung;Qi Wu

引用次数: 0

PipeOptim: Ensuring Effective 1F1B Schedule With Optimizer-Dependent Weight Prediction PipeOptim：通过优化器依赖的权重预测确保有效的1F1B计划

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-18 DOI: 10.1109/TKDE.2025.3543225

Lei Guan;Dongsheng Li;Yongle Chen;Jiye Liang;Wenjian Wang;Xicheng Lu

{"title":"PipeOptim: Ensuring Effective 1F1B Schedule With Optimizer-Dependent Weight Prediction","authors":"Lei Guan;Dongsheng Li;Yongle Chen;Jiye Liang;Wenjian Wang;Xicheng Lu","doi":"10.1109/TKDE.2025.3543225","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543225","url":null,"abstract":"Asynchronous pipeline model parallelism with a “1F1B” (one forward, one backward) schedule generates little bubble overhead and always provides quite a high throughput. However, the “1F1B” schedule inevitably leads to weight inconsistency and weight staleness issues due to the cross-training of different mini-batches across GPUs. To simultaneously address these two problems, in this paper, we propose an optimizer-dependent weight prediction strategy (a.k.a PipeOptim) for asynchronous pipeline training. The key insight of our proposal is that we employ a weight prediction strategy in the forward pass to approximately ensure that each mini-batch uses consistent and staleness-free weights to compute the forward pass of the “1F1B” schedule. To be concrete, we first construct the weight prediction scheme based on the update rule of the used optimizer when training the deep neural network models. Then throughout the “1F1B” pipeline training, each mini-batch is mandated to execute weight prediction, subsequently employing the predicted weights to perform the forward pass. As a result, PipeOptim 1) inherits the advantage of the “1F1B” schedule and generates high throughput, and 2) can ensure effective parameter learning regardless of the type of the used optimizer. We conducted extensive experimental evaluations using nine different deep-learning models to verify the effectiveness of our proposal. The experiment results demonstrate that PipeOptim outperforms the other five popular pipeline approaches including GPipe, PipeDream, PipeDream-2BW, SpecTrain, and XPipe.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2831-2845"},"PeriodicalIF":8.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769558","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

Diversity-Promoting Recommendation With Dual-Objective Optimization and Dual Consideration 双目标优化、双重考虑的多样性推荐

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-18 DOI: 10.1109/TKDE.2025.3543285

Yuli Liu;Yuan Zhang

{"title":"Diversity-Promoting Recommendation With Dual-Objective Optimization and Dual Consideration","authors":"Yuli Liu;Yuan Zhang","doi":"10.1109/TKDE.2025.3543285","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543285","url":null,"abstract":"Diversifying recommendations to broaden user horizons and explore potential interests has become a prominent research area in recommender systems. Although numerous efforts have been made to enhance diverse recommendations, the trade-off between diversity and accuracy remains a significant challenge. The primary causes lie in the following two aspects: (<italic>i) the inherent goals of diversity-promoting recommendation, which are to simultaneously deliver accurate recommendations and cater to a broader spectrum of users’ interests, have not been adequately explored; and (<italic>ii) considering diversity only in the model training procedure cannot guarantee the provision of diversification services in recommender systems. In this work, we directly formulate the inherent goals of diversity-promoting recommendation as a dual-objective optimization problem by simultaneously minimizing the recommendation error and maximizing diversity. These proposed objectives are integrated into Generative Adversarial Nets (GANs) to guide the training process toward the orientation of boosting both diversification and accuracy. Additionally, we propose considering diversity in both training and serving phases. Experimental results demonstrate that our model outperforms others in both diversity and relevance. We extend DDPR to state-of-the-art CTR and re-ranking models, which also result in improved performance on these tasks, further demonstrating the applicability of our model in real-world scenarios.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2391-2404"},"PeriodicalIF":8.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769389","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

A Causal-Based Attribute Selection Strategy for Conversational Recommender Systems 基于因果关系的会话推荐系统属性选择策略

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-18 DOI: 10.1109/TKDE.2025.3543112

Dianer Yu;Qian Li;Xiangmeng Wang;Guandong Xu

{"title":"A Causal-Based Attribute Selection Strategy for Conversational Recommender Systems","authors":"Dianer Yu;Qian Li;Xiangmeng Wang;Guandong Xu","doi":"10.1109/TKDE.2025.3543112","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543112","url":null,"abstract":"Conversational recommender systems (CRSs) provide personalised recommendations by strategically querying attributes matching users’ preferences. However, this process suffers from confounding effects of time and user attributes, as users’ preferences naturally evolve over time and differ among similar users due to their unique attributes. These confounding effects distort user behaviors’ causal drivers, challenging CRSs in learning users’ true preferences and generalizable patterns. Recently, causal inference provides principled tools to clarify cause-effect relations in data, offering a promising way to address such confounding effects. In this context, we introduce <bold>Causal <bold>Conversational <bold>Recommender (<bold>CCR), which applies causal inference to model the causality between user behaviors and time/user attribute, enabling deeper understanding of user behaviors’ causal drivers. First, CCR employs stratification and matching to ensure attribute asked per round is independent from time and user attributes, mitigating their confounding effects. Following that, we apply the Average Treatment Effect (ATE) to quantify the unbiased causal impact of each unasked attribute on user preferences, identifying the attribute with the highest ATE per round as the causal-based attribute, i.e., causal driver of user behaviour. Finally, CCR iteratively refines user preferences through feedback on causal-based attributes. Extensive experiments verified CCR's robustness and personalization.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2169-2182"},"PeriodicalIF":8.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769394","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

One-Step Adaptive Graph Learning for Incomplete Multiview Subspace Clustering 不完全多视图子空间聚类的一步自适应图学习

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-18 DOI: 10.1109/TKDE.2025.3543696

Jie Chen;Hua Mao;Wai Lok Woo;Chuanbin Liu;Zhu Wang;Xi Peng

{"title":"One-Step Adaptive Graph Learning for Incomplete Multiview Subspace Clustering","authors":"Jie Chen;Hua Mao;Wai Lok Woo;Chuanbin Liu;Zhu Wang;Xi Peng","doi":"10.1109/TKDE.2025.3543696","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543696","url":null,"abstract":"Incomplete multiview clustering (IMVC) optimally integrates complementary information within incomplete multiview data to improve clustering performance. Several one-step graph-based methods show great potential for IMVC. However, the low-rank structures of similarity graphs are neglected at the initialization stage of similarity graph construction. Moreover, further investigation into complementary information integration across incomplete multiple views is needed, particularly when considering the low-rank structures implied in high-dimensional multiview data. In this paper, we present one-step adaptive graph learning (OAGL) that adaptively performs spectral embedding fusion to achieve clustering assignments at the clustering indicator level. We first initiate affinity matrices corresponding to incomplete multiple views using spare representation under two constraints, i.e., the sparsity constraint on each affinity matrix corresponding to an incomplete view and the degree matrix of the affinity matrix approximating an identity matrix. This approach promotes exploring complementary information across incomplete multiple views. Subsequently, we perform an alignment of the spectral block-diagonal matrices among incomplete multiple views using low-rank tensor learning theory. This facilitates consistency information exploration across incomplete multiple views. Furthermore, we present an effective alternating iterative algorithm to solve the resulting optimization problem. Extensive experiments on benchmark datasets demonstrate that the proposed OAGL method outperforms several state-of-the-art approaches.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2771-2783"},"PeriodicalIF":8.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769455","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

CMVC+: A Multi-View Clustering Framework for Open Knowledge Base Canonicalization Via Contrastive Learning CMVC+：基于对比学习的开放知识库规范化多视图聚类框架

IF 8.9 2区计算机科学

IEEE Transactions on Knowledge and Data Engineering Pub Date : 2025-02-18 DOI: 10.1109/TKDE.2025.3543423

Yang Yang;Wei Shen;Junfeng Shu;Yinan Liu;Edward Curry;Guoliang Li

{"title":"CMVC+: A Multi-View Clustering Framework for Open Knowledge Base Canonicalization Via Contrastive Learning","authors":"Yang Yang;Wei Shen;Junfeng Shu;Yinan Liu;Edward Curry;Guoliang Li","doi":"10.1109/TKDE.2025.3543423","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3543423","url":null,"abstract":"Open information extraction (OIE) methods extract plenty of OIE triples <italic><inline-formula><tex-math>$< $</tex-math><alternatives><mml:math><mml:mo><</mml:mo></mml:math><inline-graphic></alternatives></inline-formula>noun phrase, relation phrase, noun phrase<inline-formula><tex-math>$> $</tex-math><alternatives><mml:math><mml:mo>></mml:mo></mml:math><inline-graphic></alternatives></inline-formula> from unstructured text, which compose large open knowledge bases (OKBs). Noun phrases and relation phrases in such OKBs are not canonicalized, which leads to scattered and redundant facts. It is found that two views of knowledge (i.e., a fact view based on the fact triple and a context view based on the fact triple's source context) provide complementary information that is vital to the task of OKB canonicalization, which clusters synonymous noun phrases and relation phrases into the same group and assigns them unique identifiers. In order to leverage these two views of knowledge jointly, we propose CMVC+, a novel unsupervised framework for canonicalizing OKBs without the need for manually annotated labels. Specifically, we propose a multi-view CHF K-Means clustering algorithm to mutually reinforce the clustering of view-specific embeddings learned from each view by considering the clustering quality in a fine-grained manner. Furthermore, we propose a novel contrastive learning module to refine the learned view-specific embeddings and further enhance the canonicalization performance. We demonstrate the superiority of our framework through extensive experiments on multiple real-world OKB data sets against state-of-the-art methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 5","pages":"2296-2310"},"PeriodicalIF":8.9,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769450","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