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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Mingyu Wang, Yijie Zhou, Xiaohui Wang, Yongjun National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies between variables within the sliding window and effectively leveraging them. Existing studies rely on domain knowledge to pre-set the window size, and overlook the strength of dependencies while calculating direction based on variable similarity. This paper proposes GSLTE, a graph structure learning method for MTAD. GSLTE employs Fast Fourier Transform to conduct iterative segmentation of the whole series, selecting the dominant Fourier frequency as the window size for each subsequence within the minimum interval. GSLTE quantifies the direction and strength of the dependencies based on variable-lag transfer entropy which is achieved through Dynamic Time Warping method to learn asymmetric links between variables. Extensive experiments show that GNN-based MTAD methods applying GSLTE can further improve anomaly detection performance while outperforming state-of-the-art competitors. © 2025 IEEE.

关键词： Anomaly detection graph structure learning Multivariate time series Window size selection

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Uni-directional graph structure learning-based multivariate time series anomaly detection with dynamic prior knowledge

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INTERNATIONAL JOURNAL OF MACHINE learning AND CYBERNETICS 2025年第1期16卷 267-283页

作者： He, Shiming Li, Genxin Wang, Jin Xie, Kun Sharma, Pradip Kumar Changsha Univ Sci & Technol Key Lab Safety Control Bridge Engn Minist Educ Changsha 410114 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Univ Aberdeen Comp Sci Aberdeen AB24 3FX Scotland

In the Internet of Things (IoT) system, sensors generate a vast amount of multivariate time series data and transmit it to the data center for aggregation and analysis. However, due to equipment failure or attacks, the collected data may contain anomalies, which in turn affect the overall performance and reliability of IoT services. Therefore, an effective multivariate time series anomaly detection (MTSAD) method is a crucial issue to ensure the quality of service. graph structure learning (GSL)-based methods become a promising technology in MTSAD, which learns an optimal graph structure joint with the anomaly detection task. However, most existing methods disregard the causal and dynamic relationships between sensors during the processing of IoT and assume that the data is devoid of any missing values. Therefore, we propose a uni-direction graph structure learning-based multivariate time-series anomaly detection with dynamic prior knowledge (DPGLAD), which learns the uni-directional relationships between sensors under the constraint of the dynamic prior graph and utilizes diffusion convolutional recurrent neural networks (DCRNN) based on timestamp mask to extract temporal and spatial features. Extensive experiments show that our method has better detection performance and shorter training times than state-of-the-art techniques on four real-world datasets. Compared with the best GSL-based method GTA, DPGLAD achieves 4.16-7.29% more F1-score.

关键词： Multivariate time series Anomaly detection graph structure learning graph neural network Dynamic prior graph Uni-directional graph structure

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Anomaly Detection Over Multi-Relational graphs Using graph structure learning and Multi-Scale Meta-Path graph Aggregation

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IEEE ACCESS 2025年 13卷 60303-60316页

作者： Zhang, Chi Jeong, Junho Jung, Jin-Woo Dongguk Univ Dept Comp Sci & Engn Seoul 04620 South Korea

graph Neural Networks (GNNs) have recently achieved remarkable success in various learning tasks involving graph-structured data. However, their application to multi-relational graph anomaly detection problems on real-world datasets presents several challenges that significantly hinder performance: the structural noise and inconsistencies inherent in real-world graph data, difficulties in aggregating information across multiple relation types, and the imbalanced class labels. To address these limitations, we introduce a graph structure learning layer designed to refine the original, noisy graph structure, enhancing the representation of node relationships. This enables our model to effectively handle inconsistencies and structural noise present in the original graph data with the augmented graph topology. To further exploit the semantic information embedded within the multi-relational graph data, we developed a multi-scale aggregation layer that facilitates information propagation and fusion through multi-relational levels, utilizing automatically and implicitly extracted meta-path graphs. Additionally, to handle the impact of imbalanced class labels, which often degrades the classification performance of GNN-based approaches, we introduce a score reweighting mechanism that balances probability scores between minority and majority classes. This strategy improves the model's learning ability, leading to improved class distinction. Comprehensive experiments are conducted using two popular real-world review datasets to validate the effectiveness of our proposed model. Empirical results demonstrate that our approach consistently outperforms state-of-the-art baselines across three evaluation metrics, namely Average Precision, AUC and GMean.

关键词： Anomaly detection Noise Data models Training Reviews Image edge detection Weight measurement Noise measurement Message passing graph anomaly detection graph neural networks graph structure learning meta-path graphs meta-path graphs

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A novel spatial-temporal graph convolution network based on temporal embedding graph structure learning for multivariate time series prediction

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 141卷

作者： Lei, Tianyang Li, Jichao Yang, Kewei Gong, Chang Natl Univ Def Technol Coll Syst Engn Changsha 410000 Peoples R China

The prediction of multivariate time series is a pivotal research area in data mining, offering extensive practical applications in many real-world scenarios, including transportation, finance, energy systems, the Internet of Things. Accurately predicting multivariate time series is challenging due to the complex temporal and spatial dependencies among variables. To tackle this challenge, this study proposed a deep learning model utilizing graph neural networks for predicting multivariate time series. Specifically, a multivariate time series is modeled as a graph, with nodes representing variables, edges indicating their interdependencies, and the time series data serving as node attributes. We leverage the temporal convolutional network to construct a graph structure learning module that captures the underlying dependencies between variables through the learned adjacency matrix. The prediction model was built by integrating Long Short-Term Memory networks and graph neural networks, enabling the simultaneous capture of temporal and spatial dependencies in multivariate time series data. Additionally, to mitigate the issue of over-smoothing in graph neural networks, we incorporated the Laplacian sharpening technique into our model. The proposed method is generalizable for handling multivariate time series data, as it does not require a pre-defined adjacency matrix among variables. We empirically evaluated the performance of our method through extensive experiments conducted on six real-world datasets, the experimental results indicated that our method could effectively improve the accuracy of multivariate time series prediction.

关键词： Multivariate time series graph neural networks graph structure learning Laplacian sharpening

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GSL-Mash: Enhancing Mashup Creation Service Recommendations Through graph structure learning 22nd

GSL-Mash: Enhancing Mashup Creation Service Recommendations ...

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22nd International Conference on Service Oriented Computing

作者： Liu, Sihao Liu, Mingyi Jiang, Tianyu Yu, Shuang Xu, Hanchuan Wang, Zhongjie Harbin Inst Technol Fac Comp Harbin Peoples R China

ISBN: (纸本)9789819608072;9789819608089

The proliferation of Web APIs has facilitated the creation of numerous software applications through the integration of diverse services, commonly referred to as mashups. However, the growing complexity and number of available Web APIs pose significant challenges in API services selection. Current service recommendation models, predominantly based on graph Neural Networks (GNNs), often underperform due to the simplistic and overly complex APIs co-occurrence graphs they utilize, which impede both efficiency and performance. This paper introduces a novel model, GSL-Mash, which incorporates graph structure learning (GSL) to optimize graph data in service recommendations. By refining the graph structure to retain only pertinent connections, our model significantly reduces unnecessary complexity and noise, enhancing both the efficacy and accuracy of service recommendations. We validate GSL-Mash using real-world datasets from ProgrammableWeb, where it outperforms established baselines with up to 45.39% improvement on NDCG@10 metric. Additionally, we contribute to the academic and development communities by making our implementation publicly available. This study not only advances the technology of service recommendation systems but also sets a foundational approach for future research in optimizing graph-based service recommendation models.

关键词： Service recommendation Mashup creation graph Neural Network graph structure learning

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Uncertainty-Aware graph structure learning 25

Uncertainty-Aware Graph Structure Learning

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34th ACM Web Conference, WWW 2025

作者： Han, Shen Zhou, Zhiyao Chen, Jiawei Hao, Zhezheng Zhou, Sheng Wang, Gang Feng, Yan Chen, Chun Wang, Can The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security China Northwestern Polytechnical University Xi’an China Bangsun Technology Hangzhou China

ISBN: (纸本)9798400712746

graph Neural Networks (GNNs) have become a prominent approach for learning from graph-structured data. However, their effectiveness can be significantly compromised when the graph structure is suboptimal. To address this issue, graph structure learning (GSL) has emerged as a promising technique that refines node connections adaptively. Nevertheless, we identify two key limitations in existing GSL methods: 1) Most methods primarily focus on node similarity to construct relationships, while overlooking the quality of node information. Blindly connecting low-quality nodes and aggregating their ambiguous information can degrade the performance of other nodes. 2) The constructed graph structures are often constrained to be symmetric, which may limit the model’s flexibility and effectiveness. To overcome these limitations, we propose an Uncertainty-aware graph structure learning (UnGSL) strategy. UnGSL estimates the uncertainty of node information and utilizes it to adjust the strength of directional connections, where the influence of nodes with high uncertainty is adaptively reduced. Importantly, UnGSL serves as a plug-in module that can be seamlessly integrated into existing GSL methods with minimal additional computational cost. In our experiments, we implement UnGSL into six representative GSL methods, demonstrating consistent performance improvements. © 2025 Copyright held by the owner/author(s).

关键词： graph Neural Network graph structure learning Uncertainty

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graph structure learning-Based Multivariate Time Series Anomaly Detection in Internet of Things for Human-Centric Consumer Applications

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第3期70卷 5419-5431页

作者： He, Shiming Li, Genxin Yi, Tongzhijian Alfarraj, Osama Tolba, Amr Sangaiah, Arun Kumar Sherratt, R. Simon Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Peoples R China Changsha Univ Sci & Technol Hunan Prov Key Lab Intelligent Proc Big Data Trans Changsha 410114 Peoples R China King Saud Univ Community Coll Comp Sci Dept Riyadh 11437 Saudi Arabia Natl Yunlin Univ Sci & Technol Int Grad Sch AI Touliu 64002 Taiwan Sunway Univ Sch Engn & Technol Petaling Jaya 47500 Malaysia Univ Reading Dept Biomed Engn Reading RG6 6AY England

As the Internet of Things system becomes more popular and ubiquitous, it has also gradually entered the consumer electronics field. For example, smart home systems have numerous sensors that monitor the environment and interact with the Internet to provide smart services. A large amount of multivariate time series data generated using sensors can provide services for consumers and identify faulty systems through multivariate time series anomaly detection (MTSAD), which is crucial for maintaining system stability. However, representing the complex relationships among multivariate time series is challenging. Recently, graph neural networks and graph structure learning, which can excellently learn complex time series relationships, have been applied to multivariate time series. However, existing research on graph structure learning only constructs k-Nearest Neighbor (kNN) graphs based on the pair-wise similarity between time series. This generates a quadratic cost and only considers partial relationships among sensors. Accordingly, we propose a lightweight graph structure learning-based multivariate time series anomaly detection (GSLAD), which exploits full graph parameterization to learn the graph structure without pair-wise similarity to overcome the quadratic cost and the limited neighbor relationship. GSLAD exploits diffusion convolutional recurrent neural network (DCRNN) to extract temporal and spatial features. The results from the extensive simulations performed on four public real-world datasets demonstrate that the F1 score improved by an average of 5% with less training time compared to existing state-of-the-art methods.

关键词： Time series analysis Feature extraction Anomaly detection Costs Correlation graph neural networks Consumer electronics graph attention neural network graph structure learning multivariate time series

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graph structure learning With Automatic Search of Hyperparameters Based on Genetic Programming

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第6期8卷 4155-4164页

作者： Wang, Pengda Lu, Mingjie Yan, Weiqing Yang, Dong Liu, Zhaowei Univ Sci & Technol China Hefei 230026 Anhui Peoples R China Yantai Univ Sch Comp & Control Engn Yantai 264005 Peoples R China Georgia State Univ Dept Comp Sci Atlanta GA 30303 USA

graph neural networks (GNNs) rely heavily on graph structures and artificial hyperparameters, which may increase computation and affect performance. Most GNNs use original graphs, but the original graph data has problems with noise and incomplete information, which easily leads to poor GNN performance. For this kind of problem, recent graph structure learning methods consider how to generate graph structures containing label information. The settings of some hyperparameters will also affect the expression of the GNN model. This paper proposes a genetic graph structure learning method (Genetic-GSL). Different from the existing graph structure learning methods, this paper not only optimizes the graph structure but also the hyperparameters. Specifically, different graph structures and different hyperparameters are used as parents;the offspring are cross-mutated through the parents;and then excellent offspring are selected through evaluation to achieve dynamic fitting of the graph structure and hyperparameters. Experiments show that, compared with other methods, Genetic-GSL basically improves the performance of node classification tasks by 1.2%. With the increase in evolution algebra, Genetic-GSL has good performance on node classification tasks and resistance to adversarial attacks.

关键词： graph neural networks hyperparameters optimization graph structure learning genetic model

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graph structure learning based on feature and label consistency

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INTELLIGENT DATA ANALYSIS 2022年第6期26卷 1539-1555页

作者： Yuan, Jinliang Yao, Yirong Xu, Ming Yu, Hualei Xie, Junyuan Wang, Chongjun Nanjing Univ Dept Comp Sci & Technol Nanjing Jiangsu Peoples R China Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

graph Neural Networks (GNNs) have achieved remarkable success in graph-related tasks by combining node features and graph topology elegantly. Most GNNs assume that the networks are homophilous, which is not always true in the real world, i.e., structure noise or disassortative graphs. Only a few works focus on generalizing graph neural networks to heterophilous or low homophilous networks, where connected nodes may have different labels. In this paper, we design a simple and effective graph structure learning strategy based on Feature and Label consistency (GSLFL) to increase the homophilous level of networks for generalizing any existing GNNs to heterophilous networks. Specifically, we first introduce a method to learn graph structure based on node features and then modify the graph structure based on label consistency. Further, we combine the GSLFL with three existing GNNs to learn node representations and graph structure together. And we design a self-training method to iteratively train models and modify graph structure with pseudo-labels. Finally, our empirical results on 6 public networks with homophily or heterophily, and structure attacks show that our methods outperform the state-of-the-art methods in most cases.

关键词： graph neural networks graph structure learning structure attacks heterophily

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A graph structure-Feature learning Network for Diagnosing Alzheimer’s Disease Based on Multi-modal Brain Biometric Feature 18th

A Graph Structure-Feature Learning Network for Diagnosing A...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Shang, Dongxu Wang, Huabin Zhang, Mengxin Peng, Yuhang Ye, Xingjian Wang, Zilin Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Anhui Hefei China Stony Brook Institute at Anhui University Anhui Hefei China

ISBN: (纸本)9789819610709

Alzheimer’s Disease (AD) is a neurodegenerative disease, typically identified and diagnosed through brain biometric feature. However, certain common brain biometric feature exist among patients in different stages of AD, which result in incorrect edges within the graph structure. To address above issues, this paper proposes a graph structure-Feature learning Network (GSFLN) model. The model simultaneously learns a graph structure that appropriately represents the relationships among patients and selects the significant pathogenic brain biometric feature of patients. Firstly, a graph Convolutional Clustering (GCC) module is designed to capture the relationships between non-image data of patients. Secondly, a Pathogenic Feature Selection (PFS) module is designed to capture the relationships between image data of patients and select the pathogenic brain biometric feature. Thirdly, an Unsupervised Predictive Clustering (UPC) module is designed to control the above two modules. Our proposed model has surpassed the current state-of-the-art level in diagnostic performance on both the NACC and Tadpole datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Alzheimer’s Disease brain biometric feature Feature Selection graph Convolutional Network graph structure learning

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