IEEE Transactions on Knowledge and Data Engineering最新文献

{"title":"AISFuser: Encoding Maritime Graphical Representations With Temporal Attribute Modeling for Vessel Trajectory Prediction","authors":"Zhiwen Zhang;Wei Yuan;Zipei Fan;Xuan Song;Ryosuke Shibasaki","doi":"10.1109/TKDE.2025.3531770","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3531770","url":null,"abstract":"Maritime transportation, vital for nearly 90% of global trade, necessitates precise vessel trajectory prediction for safety and efficiency. Although the Automatic Identification System (AIS) provides a comprehensive data source, how to model these multi-modal and heterogeneous time-varying sequences (such as vessels’ kinetic information and ocean weather factors) poses a formidable challenge. Moreover, most existing approaches are limited by the confined scope of vessel trajectory modeling, making it impossible to consider the unique characteristics of maritime transportation system. To tackle these challenges, we propose a novel framework called AISFuser to i) encode unique maritime traffic network into graphical representations, and ii) introduce the heterogeneity into multi-modal temporal embeddings through Self-Supervised Learning (SSL). Specifically, our AISFuser is constructed by combining an attention-based graph block with a transformer network to encode information across space and time, respectively. In terms of temporal dimension, one SSL auxiliary task is also designed to enhance the heterogeneity of temporal representations and supplement the main vessel prediction task. We validate the effectiveness of the proposed AISFuser on a real-world AIS dataset. Extensive experimental results demonstrate that our method can forecast multiple attributes of vessel trajectory for over 10 hours into the future, outperforming competitive baselines.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1571-1584"},"PeriodicalIF":8.9,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570826","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":"AEGK: Aligned Entropic Graph Kernels Through Continuous-Time Quantum Walks","authors":"Lu Bai;Lixin Cui;Ming Li;Peng Ren;Yue Wang;Lichi Zhang;Philip S. Yu;Edwin R. Hancock","doi":"10.1109/TKDE.2024.3512181","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3512181","url":null,"abstract":"In this work, we develop a family of Aligned Entropic Graph Kernels (AEGK) for graph classification. We commence by performing the Continuous-time Quantum Walk (CTQW) on each graph structure, and compute the Averaged Mixing Matrix (AMM) to describe how the CTQW visits all vertices from a starting vertex. More specifically, we show how this AMM matrix allows us to compute a quantum Shannon entropy of each vertex for either un-attributed or attributed graphs. For pairwise graphs, the proposed AEGK kernels are defined by computing the kernel-based similarity between the quantum Shannon entropies of their pairwise aligned vertices. The analysis of theoretical properties reveals that the proposed AEGK kernels cannot only address the shortcoming of neglecting the structural correspondence information between graphs arising in most existing R-convolution graph kernels, but also overcome the problems of neglecting the structural differences and vertex-attributed information arising in existing vertex-based matching kernels. Moreover, unlike most existing classical graph kernels that only focus on the global or local structural information of graphs, the proposed AEGK kernels can simultaneously capture both global and local structural characteristics through the quantum Shannon entropies, reflecting more precise kernel-based similarity measures between pairwise graphs. The above theoretical properties explain the effectiveness of the proposed AEGK kernels. Experimental evaluations demonstrate that the proposed kernels can outperform state-of-the-art graph kernels and deep learning models for graph classification.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1064-1078"},"PeriodicalIF":8.9,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106843","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":"Mitigating the Tail Effect in Fraud Detection by Community Enhanced Multi-Relation Graph Neural Networks","authors":"Li Han;Longxun Wang;Ziyang Cheng;Bo Wang;Guang Yang;Dawei Cheng;Xuemin Lin","doi":"10.1109/TKDE.2025.3530467","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3530467","url":null,"abstract":"Fraud detection, a classical data mining problem in finance applications, has risen in significance amid the intensifying confrontation between fraudsters and anti-fraud forces. Recently, an increasing number of criminals are constantly expanding the scope of fraud activities to covet the property of innocent victims. However, most existing approaches require abundant historical records to mine fraud patterns from financial transaction behaviors, thereby leading to significant challenges to protect minority groups, who are less involved in the modern financial market but also under the threat of fraudsters nowadays. Therefore, in this paper, we propose a novel community-enhanced multi-relation graph neural network-based model, named CMR-GNN, to address the important defects of existing fraud detection models in the tail effect situation. In particular, we first construct multiple types of relation graphs from historical transactions and then devise a clustering-based neural network module to capture diverse patterns from transaction communities. To mitigate information lacking tailed nodes, we proposed tailed-groups learning modules to aggregate features from similarly clustered subgraphs by graph convolution networks. Extensive experiments on both the real-world and public datasets demonstrate that our method not only surpasses the state-of-the-art baselines but also could effectively harness information within transaction communities while mitigating the impact of tail effects.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"2029-2041"},"PeriodicalIF":8.9,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570593","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":"CloudRGK: Towards Private Similarity Measurement Between Graphs on the Cloud","authors":"Linxiao Yu;Jun Tao;Yifan Xu;Haotian Wang","doi":"10.1109/TKDE.2025.3529949","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3529949","url":null,"abstract":"Graph kernels are a significant class of tools for measuring the similarity of graph data, which is the basis of a wide range of graph learning methods. However, graph kernels often suffer from high computing overhead. With the shining of cloud computing, it is desirable to transfer the computing burden to the server with abundant computing resources to reduce the cost of local machines. Nonetheless, under the honest-but-curious cloud assumption, the server may peek at the data, raising privacy concerns. To eliminate the risk of data privacy leakage, we propose CloudRGK to securely perform Random walk Graph Kernel(RGK), one of the most well-known graph kernels, on the cloud. We first prove that the edge- and vertex-labeled graphs could be transformed into an equivalent matrix representation. Afterward, we prove that the cloud could perform the core operations in RGK on the encrypted graphs without feature information loss. Evaluations of the real-world graph data demonstrate that our strategy significantly reduces the overhead of the local party to perform RGK without performance degradation. Meanwhile, it introduces only a small amount of extra computation cost. To the best of our knowledge, it is the first work towards private graph kernel computation on the cloud.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1688-1701"},"PeriodicalIF":8.9,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Generalized $f$f-Divergence With Applications in Pattern Classification","authors":"Fuyuan Xiao;Weiping Ding;Witold Pedrycz","doi":"10.1109/TKDE.2025.3530524","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3530524","url":null,"abstract":"In multisource information fusion (MSIF), Dempster–Shafer evidence (DSE) theory offers a useful framework for reasoning under uncertainty. However, measuring the divergence between belief functions within this theory remains an unresolved challenge, particularly in managing conflicts in MSIF, which is crucial for enhancing decision-making level. In this paper, several divergence and distance functions are proposed to quantitatively measure discrimination between belief functions in DSE theory, including the reverse evidential KullbackLeibler (REKL) divergence, evidential Jeffrey’s (EJ) divergence, evidential JensenShannon (EJS) divergence, evidential <inline-formula><tex-math>$chi ^{2}$</tex-math></inline-formula> (E<inline-formula><tex-math>$chi ^{2}$</tex-math></inline-formula>) divergence, evidential symmetric <inline-formula><tex-math>$chi ^{2}$</tex-math></inline-formula> (ES<inline-formula><tex-math>$chi ^{2}$</tex-math></inline-formula>) divergence, evidential triangular (ET) discrimination, evidential Hellinger (EH) distance, and evidential total variation (ETV) distance. On this basis, a generalized <inline-formula><tex-math>$f$</tex-math></inline-formula>-divergence, also called the evidential <inline-formula><tex-math>$f$</tex-math></inline-formula>-divergence (Ef divergence), is proposed. Depending on different kernel functions, the Ef divergence degrades into several specific classes: EKL, REKL, EJ, EJS, E<inline-formula><tex-math>$chi ^{2}$</tex-math></inline-formula> and ES<inline-formula><tex-math>$chi ^{2}$</tex-math></inline-formula> divergences, ET discrimination, and EH and ETV distances. Notably, when basic belief assignments (BBAs) are transformed into probability distributions, these classes of Ef divergence revert to their classical counterparts in statistics and information theory. In addition, several Ef-MSIF algorithms are proposed for pattern classification based on the classes of Ef divergence. These Ef-MSIF algorithms are evaluated on real-world datasets to demonstrate their practical effectiveness in solving classification problems. In summary, this work represents the first attempt to extend classical <inline-formula><tex-math>$f$</tex-math></inline-formula>-divergence within the DSE framework, capitalizing on the distinct properties of BBA functions. Experimental results show that the proposed Ef-MSIF algorithms improve classification accuracy, with the best-performing Ef-MSIF algorithm achieving an overall performance difference approximately 1.22 times smaller than the suboptimal method and 14.12 times smaller than the worst-performing method.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1556-1570"},"PeriodicalIF":8.9,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570592","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":"EA$^{2}$2N: Evidence-Based AMR Attention Network for Fake News Detection","authors":"Shubham Gupta;Abhishek Rajora;Suman Kundu","doi":"10.1109/TKDE.2025.3529707","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3529707","url":null,"abstract":"Proliferation of fake news has become a critical issue in today's information-driven society. Our study includes external knowledge from Wikidata which allows the model to cross-reference factual claims with established knowledge. This approach deviates from the reliance on social information to detect fake news that many state-of-the-art (SOTA) fact-checking models adopt. This paper introduces <b>EA<inline-formula><tex-math>$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula>N</b>, an <b>E</b>vidence-based <b>A</b>MR (abstract meaning representation) <b>A</b>ttention <b>N</b>etwork for Fake News Detection. EA<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>N utilizes the proposed Evidence based Abstract Meaning Representation (WikiAMR) which incorporates knowledge using a proposed evidence-linking algorithm, pushing the boundaries of fake news detection. The proposed framework encompasses a combination of a novel language encoder and a graph encoder to detect fake news. While the language encoder effectively combines transformer-encoded textual features with affective lexical features, the graph encoder encodes semantic relations with evidence through external knowledge, referred to as WikiAMR graph. A path-aware graph learning module is designed to capture crucial semantic relationships among entities over evidence. Extensive experiments support our model's superior performance, surpassing SOTA methodologies with a difference of 2-3% in F1-score and accuracy for Politifact and Gossipcop datasets. The improvement due to the introduction of WikiAMR is found to be statistically significant with t-value less than 0.01.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1742-1752"},"PeriodicalIF":8.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570800","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":"MHR: A Multi-Modal Hyperbolic Representation Framework for Fake News Detection","authors":"Shanshan Feng;Guoxin Yu;Dawei Liu;Han Hu;Yong Luo;Hui Lin;Yew-Soon Ong","doi":"10.1109/TKDE.2025.3528951","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3528951","url":null,"abstract":"The rapid growth of the internet has led to an alarming increase in the dissemination of fake news, which has had many negative effects on society. Various methods have been proposed for detecting fake news. However, these approaches suffer from several limitations. First, most existing works only consider news as separate entities and do not consider the correlations between fake news and real news. Moreover, these works are usually conducted in the Euclidean space, which is unable to capture complex relationships between news, in particular the hierarchical relationships. To tackle these issues, we introduce a novel <underline>M</u>ulti-modal <underline>H</u>yperbolic <underline>R</u>epresentation framework (MHR) for fake news detection. Specifically, we capture the correlations between news for graph construction to arrange and analyze different news. To fully utilize the multi-modal characteristics, we first extract the textual and visual information, and then design a Lorentzian multi-modal fusion module to fuse them as the node information in the graph. By utilizing the fully hyperbolic graph neural networks, we learn the graph’s representation in hyperbolic space, followed by a detector for detecting fake news. The experimental results on three real-world datasets demonstrate that our proposed MHR model achieves state-of-the-art performance, indicating the benefits of hyperbolic representation.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"2015-2028"},"PeriodicalIF":8.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570802","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":"Dual Enhanced Meta-Learning With Adaptive Task Scheduler for Cold-Start Recommendation","authors":"Dongxiao He;Jiaqi Cui;Xiaobao Wang;Guojie Song;Yuxiao Huang;Lingfei Wu","doi":"10.1109/TKDE.2025.3529525","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3529525","url":null,"abstract":"Recommendation systems typically rely on users’ historical behavior to infer their preferences. However, when new entries emerge, the system cannot make accurate prediction due to the lack of historical data. This is known as the “cold-start” problem, which not only limits the exposure of new items but also impacts the first experience of new users severely. Meta-learning has emerged as a promising approach to address this issue, but existing methods have limitations in dealing with the differences in user preferences and sparse monitoring data. To overcome these limitations, Dual enhanced Meta-learning with Adaptive Task Sampling is proposed. First, we propose an embedding enhancement strategy for cold nodes. Specifically, we map the cold-start embeddings into the warm space based on the common features shared across all nodes, and then add uniform noise to create the contrastive views. This strategy injects warm co-occurrence signals into the content of cold nodes, effectively enriching the feature space of cold nodes. Second, we introduce an adaptive task scheduler to measure the effectiveness of different meta-tasks and filter out the noise from invalid tasks. We assign different sampling probabilities to the tasks based on the learning process (gradient similarity) and the learning result (loss) of the meta-tasks. Finally, we consider the above two modules as auxiliary tasks for the main meta-model. Then, joint optimization is carried out through a multi-task learning framework. Experiments in three cold-start scenarios show that our approach outperforms the most advanced baselines, including traditional methods, HIN-based methods, and meta-learning-based methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1728-1741"},"PeriodicalIF":8.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570596","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":"Spatio-Temporal Prediction on Streaming Data: A Unified Federated Continuous Learning Framework","authors":"Hao Miao;Yan Zhao;Chenjuan Guo;Bin Yang;Kai Zheng;Christian S. Jensen","doi":"10.1109/TKDE.2025.3528876","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3528876","url":null,"abstract":"The widespread deployment of wireless and mobile devices results in a proliferation of decentralized spatio-temporal data. Many recent proposals that target deep learning for spatio-temporal prediction assume that all data is available at a central location and suffers from so-called catastrophic forgetting, where previously learned knowledge is entirely forgotten when new data arrives. Such proposals may face data privacy concerns and may experience deteriorating prediction performance when applied in decentralized settings where data streams into the system. To bridge the gap between decentralized training and spatio-temporal prediction on streaming data, we propose a unified federated continuous learning framework, which uses a horizontal federated learning mechanism for protecting data privacy and includes a global replay buffer with synthetic spatio-temporal data generated by the previously learned global model. For each client, we fuse the current training data with synthetic spatio-temporal data using a spatio-temporal mixup mechanism to preserve historical knowledge effectively, thus avoiding catastrophic forgetting. To enable holistic representation preservation, the local models at clients each integrates a general spatio-temporal autoencoder with a spatio-temporal simple siamese network that aims to ensure prediction accuracy and avoid holistic feature loss. Extensive experiments on real data offer insight into the effectiveness of the proposed framework.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"2126-2140"},"PeriodicalIF":8.9,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570838","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":"LLM-Driven Causal Discovery via Harmonized Prior","authors":"Taiyu Ban;Lyuzhou Chen;Derui Lyu;Xiangyu Wang;Qinrui Zhu;Huanhuan Chen","doi":"10.1109/TKDE.2025.3528461","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3528461","url":null,"abstract":"Traditional domain-specific causal discovery relies on expert knowledge to guide the data-based structure learning process, thereby improving the reliability of recovered causality. Recent studies have shown promise in using the Large Language Model (LLM) as causal experts to construct autonomous expert-guided causal discovery systems through causal reasoning between pairwise variables. However, their performance is hampered by inaccuracies in aligning LLM-derived causal knowledge with the actual causal structure. To address this issue, this paper proposes a novel LLM-driven causal discovery framework that limits LLM’s prior within a reliable range. Instead of pairwise causal reasoning that requires both precise and comprehensive output results, the LLM is directed to focus on each single aspect separately. By combining these distinct causal insights, a unified set of structural constraints is created, termed a harmonized prior, which draws on their respective strengths to ensure prior accuracy. On this basis, we introduce plug-and-play integrations of the harmonized prior into mainstream categories of structure learning methods, thereby enhancing their applicability in practical scenarios. Evaluations on real-world data demonstrate the effectiveness of our approach.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 4","pages":"1943-1960"},"PeriodicalIF":8.9,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570613","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}