Pub Date : 2025-01-27DOI: 10.1109/TKDE.2024.3493391
Jie Liu;Chunhai Zhang;Zhicheng He;Wenzheng Zhang;Na Li
Network Representation Learning (NRL) has achieved remarkable success in learning low-dimensional representations for network nodes. However, most NRL methods, including Graph Neural Networks (GNNs) and their variants, face critical challenges. First, labeled network data, which are required for training most GNNs, are expensive to obtain. Second, existing methods are sub-optimal in preserving comprehensive topological information, including structural and positional information. Finally, most GNN approaches ignore the rich node content information. To address these challenges, we propose a self-supervised Network-to-Network framework (Net2Net) to learn semantically meaningful node representations. Our framework employs a pretext task of node position prediction (PosPredict) to effectively fuse the topological and content knowledge into low-dimensional embeddings for every node in a semi-supervised manner. Specifically, we regard a network as node content and position networks, where Net2Net aims to learn the mapping between them. We utilize a multi-layer recursively composable encoder to integrate the content and topological knowledge into the egocentric network node embeddings. Furthermore, we design a cross-modal decoder to map the egocentric node embeddings into their node position identities (PosIDs) in the node position network. Extensive experiments on eight diverse networks demonstrate the superiority of Net2Net over comparable methods.
{"title":"Network-to-Network: Self-Supervised Network Representation Learning via Position Prediction","authors":"Jie Liu;Chunhai Zhang;Zhicheng He;Wenzheng Zhang;Na Li","doi":"10.1109/TKDE.2024.3493391","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3493391","url":null,"abstract":"Network Representation Learning (NRL) has achieved remarkable success in learning low-dimensional representations for network nodes. However, most NRL methods, including Graph Neural Networks (GNNs) and their variants, face critical challenges. First, labeled network data, which are required for training most GNNs, are expensive to obtain. Second, existing methods are sub-optimal in preserving comprehensive topological information, including structural and positional information. Finally, most GNN approaches ignore the rich node content information. To address these challenges, we propose a self-supervised Network-to-Network framework (Net2Net) to learn semantically meaningful node representations. Our framework employs a pretext task of node position prediction (PosPredict) to effectively fuse the topological and content knowledge into low-dimensional embeddings for every node in a semi-supervised manner. Specifically, we regard a network as node content and position networks, where Net2Net aims to learn the mapping between them. We utilize a multi-layer recursively composable encoder to integrate the content and topological knowledge into the egocentric network node embeddings. Furthermore, we design a cross-modal decoder to map the egocentric node embeddings into their node position identities (PosIDs) in the node position network. Extensive experiments on eight diverse networks demonstrate the superiority of Net2Net over comparable methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1354-1365"},"PeriodicalIF":8.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106899","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}
Web attack is a major threat to cyberspace security, so web attack detection models have become a critical task. Traditional supervised learning methods learn features of web attacks with large amounts of high-confidence labeled data, which are extremely expensive in the real world. Pre-trained models offer a novel solution with their ability to learn generic features on large unlabeled datasets. However, designing and deploying a pre-trained model for real-world web attack detection remains challenges. In this paper, we present a pre-trained model for web attack detection, including a pre-processing module, a pre-training module, and a deployment scheme. Our model significantly improves classification performance on several web attack detection datasets. Moreover, we deploy the model in real-world systems and show its potential for industrial applications.
{"title":"Web-FTP: A Feature Transferring-Based Pre-Trained Model for Web Attack Detection","authors":"Zhenyu Guo;Qinghua Shang;Xin Li;Chengyi Li;Zijian Zhang;Zhuo Zhang;Jingjing Hu;Jincheng An;Chuanming Huang;Yang Chen;Yuguang Cai","doi":"10.1109/TKDE.2024.3512793","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3512793","url":null,"abstract":"Web attack is a major threat to cyberspace security, so web attack detection models have become a critical task. Traditional supervised learning methods learn features of web attacks with large amounts of high-confidence labeled data, which are extremely expensive in the real world. Pre-trained models offer a novel solution with their ability to learn generic features on large unlabeled datasets. However, designing and deploying a pre-trained model for real-world web attack detection remains challenges. In this paper, we present a pre-trained model for web attack detection, including a pre-processing module, a pre-training module, and a deployment scheme. Our model significantly improves classification performance on several web attack detection datasets. Moreover, we deploy the model in real-world systems and show its potential for industrial applications.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1495-1507"},"PeriodicalIF":8.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106889","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}
Pub Date : 2025-01-27DOI: 10.1109/TKDE.2025.3527173
{"title":"2024 Reviewers List","authors":"","doi":"10.1109/TKDE.2025.3527173","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3527173","url":null,"abstract":"","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1018-1029"},"PeriodicalIF":8.9,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10855178","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106886","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}
Pub Date : 2025-01-17DOI: 10.1109/TKDE.2024.3512181
Lu Bai;Lixin Cui;Ming Li;Peng Ren;Yue Wang;Lichi Zhang;Philip S. Yu;Edwin R. Hancock
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.
{"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}
Pub Date : 2025-01-10DOI: 10.1109/TKDE.2024.3452638
Jiayun Zhang;Xinyang Zhang;Dezhi Hong;Rajesh K. Gupta;Jingbo Shang
Inferring contextual information such as demographics from historical transactions is valuable to public agencies and businesses. Existing methods are data-hungry and do not work well when the available records of transactions are sparse. We consider here specifically inference of demographic information using limited historical grocery transactions from a few random trips that a typical business or public service organization may see. We propose a novel method called DemoMotif to build a network model from heterogeneous data and identify subgraph patterns (i.e., motifs) that enable us to infer demographic attributes. We then design a novel motif context selection algorithm to find specific node combinations significant to certain demographic groups. Finally, we learn representations of households using these selected motif instances as context, and employ a standard classifier (e.g., SVM) for inference. For evaluation purposes, we use three real-world consumer datasets, spanning different regions and time periods in the U.S. We evaluate the framework for predicting three attributes: ethnicity, seniority of household heads, and presence of children. Extensive experiments and case studies demonstrate that DemoMotif is capable of inferring household demographics using only a small number (e.g., fewer than 10) of random grocery trips, significantly outperforming the state-of-the-art.
{"title":"Contextual Inference From Sparse Shopping Transactions Based on Motif Patterns","authors":"Jiayun Zhang;Xinyang Zhang;Dezhi Hong;Rajesh K. Gupta;Jingbo Shang","doi":"10.1109/TKDE.2024.3452638","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3452638","url":null,"abstract":"Inferring contextual information such as demographics from historical transactions is valuable to public agencies and businesses. Existing methods are data-hungry and do not work well when the available records of transactions are sparse. We consider here specifically inference of demographic information using limited historical grocery transactions from a few random trips that a typical business or public service organization may see. We propose a novel method called \u0000<sc>DemoMotif</small>\u0000 to build a network model from heterogeneous data and identify subgraph patterns (i.e., motifs) that enable us to infer demographic attributes. We then design a novel motif context selection algorithm to find specific node combinations significant to certain demographic groups. Finally, we learn representations of households using these selected motif instances as context, and employ a standard classifier (e.g., SVM) for inference. For evaluation purposes, we use three real-world consumer datasets, spanning different regions and time periods in the U.S. We evaluate the framework for predicting three attributes: ethnicity, seniority of household heads, and presence of children. Extensive experiments and case studies demonstrate that \u0000<sc>DemoMotif</small>\u0000 is capable of inferring household demographics using only a small number (e.g., fewer than 10) of random grocery trips, significantly outperforming the state-of-the-art.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 2","pages":"572-583"},"PeriodicalIF":8.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142940718","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}
Multi-Modal Knowledge Graphs (MMKGs), comprising relational triples and related multi-modal data (e.g., text and images), usually suffer from the problems of low coverage and incompleteness. To mitigate this, existing studies introduce a fundamental MMKG fusion task, i.e., Multi-Modal Entity Alignment (MMEA) that identifies equivalent entities across multiple MMKGs. Despite MMEA’s significant advancements, effectively integrating MMKGs remains challenging, mainly stemming from two core limitations: 1) entity ambiguity, where real-world entities across different MMKGs may possess multiple corresponding counterparts or alternative identities; and 2) severe noise within multi-modal data. To tackle these limitations, a new task MMER (Multi-Modal Entity Resolution), which expands the scope of MMEA to encompass entity ambiguity, is introduced. To tackle this task effectively, we develop a novel model ADMH-ER (Adaptive Denoising Multi-modal Hybrid for Entity Resolution) that incorporates several crucial modules: 1) multi-modal knowledge encoders, which are crafted to obtain entity representations based on multi-modal data sources; 2) an adaptive denoising multi-modal hybrid module that is designed to tackle challenges including noise interference, multi-modal heterogeneity, and semantic irrelevance across modalities; and 3) a hierarchical multi-objective learning strategy, which is proposed to ensure diverse convergence capabilities among different learning objectives. Experimental results demonstrate that ADMH-ER outperforms state-of-the-art methods.
{"title":"ADMH-ER: Adaptive Denoising Multi-Modal Hybrid for Entity Resolution","authors":"Qian Zhou;Wei Chen;Li Zhang;An Liu;Junhua Fang;Lei Zhao","doi":"10.1109/TKDE.2025.3526623","DOIUrl":"https://doi.org/10.1109/TKDE.2025.3526623","url":null,"abstract":"Multi-Modal Knowledge Graphs (MMKGs), comprising relational triples and related multi-modal data (e.g., text and images), usually suffer from the problems of low coverage and incompleteness. To mitigate this, existing studies introduce a fundamental MMKG fusion task, i.e., Multi-Modal Entity Alignment (MMEA) that identifies equivalent entities across multiple MMKGs. Despite MMEA’s significant advancements, effectively integrating MMKGs remains challenging, mainly stemming from two core limitations: 1) entity ambiguity, where real-world entities across different MMKGs may possess multiple corresponding counterparts or alternative identities; and 2) severe noise within multi-modal data. To tackle these limitations, a new task MMER (Multi-Modal Entity Resolution), which expands the scope of MMEA to encompass entity ambiguity, is introduced. To tackle this task effectively, we develop a novel model ADMH-ER (Adaptive Denoising Multi-modal Hybrid for Entity Resolution) that incorporates several crucial modules: 1) multi-modal knowledge encoders, which are crafted to obtain entity representations based on multi-modal data sources; 2) an adaptive denoising multi-modal hybrid module that is designed to tackle challenges including noise interference, multi-modal heterogeneity, and semantic irrelevance across modalities; and 3) a hierarchical multi-objective learning strategy, which is proposed to ensure diverse convergence capabilities among different learning objectives. Experimental results demonstrate that ADMH-ER outperforms state-of-the-art methods.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1049-1063"},"PeriodicalIF":8.9,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106833","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}
Pub Date : 2025-01-03DOI: 10.1109/TKDE.2024.3525003
Can Gao;Xiaofeng Tan;Jie Zhou;Weiping Ding;Witold Pedrycz
Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index.
{"title":"Fuzzy Granule Density-Based Outlier Detection With Multi-Scale Granular Balls","authors":"Can Gao;Xiaofeng Tan;Jie Zhou;Weiping Ding;Witold Pedrycz","doi":"10.1109/TKDE.2024.3525003","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3525003","url":null,"abstract":"Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1182-1197"},"PeriodicalIF":8.9,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106814","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}
Pub Date : 2024-12-30DOI: 10.1109/TKDE.2024.3523700
Bingxu Zhang;Changjun Fan;Shixuan Liu;Kuihua Huang;Xiang Zhao;Jincai Huang;Zhong Liu
Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoretical limitations of GNNs, i.e., the GNNs expressive power. Early works in this domain mainly focus on studying the graph isomorphism recognition ability of GNNs, and recent works try to leverage the properties such as subgraph counting and connectivity learning to characterize the expressive power of GNNs, which are more practical and closer to real-world. However, no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a first survey for models for enhancing expressive power under different forms of definition. Concretely, the models are reviewed based on three categories, i.e., Graph feature enhancement, Graph topology enhancement, and GNNs architecture enhancement.
{"title":"The Expressive Power of Graph Neural Networks: A Survey","authors":"Bingxu Zhang;Changjun Fan;Shixuan Liu;Kuihua Huang;Xiang Zhao;Jincai Huang;Zhong Liu","doi":"10.1109/TKDE.2024.3523700","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3523700","url":null,"abstract":"Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoretical limitations of GNNs, i.e., the GNNs expressive power. Early works in this domain mainly focus on studying the graph isomorphism recognition ability of GNNs, and recent works try to leverage the properties such as subgraph counting and connectivity learning to characterize the expressive power of GNNs, which are more practical and closer to real-world. However, no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a first survey for models for enhancing expressive power under different forms of definition. Concretely, the models are reviewed based on three categories, i.e., Graph feature enhancement, Graph topology enhancement, and GNNs architecture enhancement.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1455-1474"},"PeriodicalIF":8.9,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106838","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}
Pub Date : 2024-12-30DOI: 10.1109/TKDE.2024.3523996
Yan Lin;Zeyu Zhou;Yicheng Liu;Haochen Lv;Haomin Wen;Tianyi Li;Yushuai Li;Christian S. Jensen;Shengnan Guo;Youfang Lin;Huaiyu Wan
Spatiotemporal trajectories are sequences of timestamped locations, which enable a variety of analyses that in turn enable important real-world applications. It is common to map trajectories to vectors, called embeddings, before subsequent analyses. Thus, the qualities of embeddings are very important. Methods for pre-training embeddings, which leverage unlabeled trajectories for training universal embeddings, have shown promising applicability across different tasks, thus attracting considerable interest. However, research progress on this topic faces two key challenges: a lack of a comprehensive overview of existing methods, resulting in several related methods not being well-recognized, and the absence of a unified pipeline, complicating the development of new methods and the analysis of methods. We present UniTE, a survey and a unified pipeline for this domain. In doing so, we present a comprehensive list of existing methods for pre-training trajectory embeddings, which includes methods that either explicitly or implicitly employ pre-training techniques. Further, we present a unified and modular pipeline with publicly available underlying code, simplifying the process of constructing and evaluating methods for pre-training trajectory embeddings. Additionally, we contribute a selection of experimental results using the proposed pipeline on real-world datasets.
{"title":"UniTE: A Survey and Unified Pipeline for Pre-Training Spatiotemporal Trajectory Embeddings","authors":"Yan Lin;Zeyu Zhou;Yicheng Liu;Haochen Lv;Haomin Wen;Tianyi Li;Yushuai Li;Christian S. Jensen;Shengnan Guo;Youfang Lin;Huaiyu Wan","doi":"10.1109/TKDE.2024.3523996","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3523996","url":null,"abstract":"Spatiotemporal trajectories are sequences of timestamped locations, which enable a variety of analyses that in turn enable important real-world applications. It is common to map trajectories to vectors, called embeddings, before subsequent analyses. Thus, the qualities of embeddings are very important. Methods for pre-training embeddings, which leverage unlabeled trajectories for training universal embeddings, have shown promising applicability across different tasks, thus attracting considerable interest. However, research progress on this topic faces two key challenges: a lack of a comprehensive overview of existing methods, resulting in several related methods not being well-recognized, and the absence of a unified pipeline, complicating the development of new methods and the analysis of methods. We present UniTE, a survey and a unified pipeline for this domain. In doing so, we present a comprehensive list of existing methods for pre-training trajectory embeddings, which includes methods that either explicitly or implicitly employ pre-training techniques. Further, we present a unified and modular pipeline with publicly available underlying code, simplifying the process of constructing and evaluating methods for pre-training trajectory embeddings. Additionally, we contribute a selection of experimental results using the proposed pipeline on real-world datasets.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1475-1494"},"PeriodicalIF":8.9,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106836","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}
Pub Date : 2024-12-27DOI: 10.1109/TKDE.2024.3523857
Yuxuan Zhou;Shang Gao;Dandan Guo;Xiaohui Wei;Jon Rokne;Hui Wang
Change point detection is crucial for identifying state transitions and anomalies in dynamic systems, with applications in network security, health care, and social network analysis. Dynamic systems are represented by dynamic graphs with spatial and temporal dimensions. As objects and their relations in a dynamic graph change over time, detecting these changes is essential. Numerous methods for change point detection in dynamic graphs have been developed, but no systematic review exists. This paper addresses this gap by introducing change point detection tasks in dynamic graphs, discussing two tasks based on input data types: detection in graph snapshot series (focusing on graph topology changes) and time series on graphs (focusing on changes in graph entities with temporal dynamics). We then present related challenges and applications, provide a comprehensive taxonomy of surveyed methods, including datasets and evaluation metrics, and discuss promising research directions.
{"title":"A Survey of Change Point Detection in Dynamic Graphs","authors":"Yuxuan Zhou;Shang Gao;Dandan Guo;Xiaohui Wei;Jon Rokne;Hui Wang","doi":"10.1109/TKDE.2024.3523857","DOIUrl":"https://doi.org/10.1109/TKDE.2024.3523857","url":null,"abstract":"Change point detection is crucial for identifying state transitions and anomalies in dynamic systems, with applications in network security, health care, and social network analysis. Dynamic systems are represented by dynamic graphs with spatial and temporal dimensions. As objects and their relations in a dynamic graph change over time, detecting these changes is essential. Numerous methods for change point detection in dynamic graphs have been developed, but no systematic review exists. This paper addresses this gap by introducing change point detection tasks in dynamic graphs, discussing two tasks based on input data types: detection in graph snapshot series (focusing on graph topology changes) and time series on graphs (focusing on changes in graph entities with temporal dynamics). We then present related challenges and applications, provide a comprehensive taxonomy of surveyed methods, including datasets and evaluation metrics, and discuss promising research directions.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 3","pages":"1030-1048"},"PeriodicalIF":8.9,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106832","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}
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