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SCG-GFFE: A Self-Constructed graph fault feature extractor based on graph Auto-encoder algorithm for unlabeled single-variable vibration signals of harmonic reducer

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ADVANCED ENGINEERING INFORMATICS 2024年第PartA期62卷

作者： Sun, Shilong Ding, Hao Zhao, Zida Xu, Wenfu Wang, Dong Harbin Inst Technol Sch Mech Engn & Automat Shenzhen 518055 Peoples R China Guangdong Key Lab Intelligent Morphing Mech & Adap Shenzhen Peoples R China Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 200240 Peoples R China Key Univ Lab Mech & Machine Theory & Intelligent U Shenzhen Peoples R China

As a pivotal component in robotic systems, harmonic reducer fault diagnosis plays a crucial role in safe and stable operation;however, the lack of labelled fault samples hampers its effectiveness. This study introduces a selfconstructed graph graph-autoencoder fault feature extractor (SCG-GFFE), a novel method that uses the graph Auto-encoder (GAE) algorithm. SCG-GFFE leverages graph Neural Networks (GNNs) for the unsupervised extraction of fault features, enhancing fault diagnosis in scenarios with limited labelled data. This approach overcomes the challenges of graph construction for fault diagnosis, particularly for single-sensor vibration signals. First, we developed the SCG-GFFE method for efficient fault feature extraction, which does not require complex domain expertise and is capable of generating graph structures. Second, we designed a suitably structured subgraph for dealing with single, unlabelled, non-multisource vibration signals. Third, a real-world harmonic reducer's vibrational signal is utilised to build an experimental study and demonstrate the effectiveness of the SCG-GFFE method. The results of SCG-GFFE demonstrate remarkable accuracy in fault diagnosis, consistently exceeding 98% across various classifiers, and indicate that the proposed method can solve the problem of limited labelled data in harmonic reducer diagnosis.

关键词： Self -constructed graph graph autoencoder Single -variable vibration signals Unsupervised fault feature extractor

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TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第2期35卷 2003-2013页

作者： Wu, Zonghan Zheng, Da Pan, Shirui Gan, Quan Long, Guodong Karypis, George Univ Technol Sydney FEIT Ctr Artificial Intelligence Ultimo NSW 2007 Australia Amazon Seattle WA 98109 USA Monash Univ Dept Data Sci & AI Clayton Vic 3800 Australia Griffith Univ Sch Informat & Commun Technol Southport Qld 4222 Australia

This article aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data. For spatial-temporal attribute entities with topological structure, the space-time is consecutive and unified while each node's current status is influenced by its neighbors' past states over variant periods of each neighbor. Most spatial-temporal neural networks for traffic forecasting study spatial dependency and temporal correlation separately in processing, gravely impaired the spatial-temporal integrity, and ignore the fact that the neighbors' temporal dependency period for a node can be delayed and dynamic. To model this actual condition, we propose TraverseNet, a novel spatial-temporal graph neural network, viewing space and time as an inseparable whole, to mine spatial-temporal graphs while exploiting the evolving spatial-temporal dependencies for each node via message traverse mechanisms. Experiments with ablation and parameter studies have validated the effectiveness of the proposed TraverseNet, and the detailed implementation can be found from https://***/nnzhan/TraverseNet.

关键词： Convolution Message passing Correlation Kernel graph neural networks Forecasting Convolutional neural networks Deep learning graph autoencoder graph convolutional networks graph neural networks (GNNs) graph representation learning network embedding

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Multi-view graph representation learning for hyperspectral image classification with spectral-spatial graph neural networks

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NEURAL COMPUTING & APPLICATIONS 2024年第7期36卷 3737-3759页

作者： Hanachi, Refka Sellami, Akrem Farah, Imed Riadh Dalla Mura, Mauro Univ Manouba RIADI Lab ENSI Manouba 2010 Tunis Tunisia Univ Lille CNRS Cent Lille UMR 9189 CRIStAL F-59000 Lille France IMT Atlantique ITI Dept 655 Ave Technopole F-29280 Plouzane Paris France Univ Grenoble Alpes Inst Engn Univ Grenoble Alpes Grenoble INP CNRSGIPSA Lab F-38000 Grenoble France Inst Univ France IUF F-75231 Paris France

Hyperspectral image (HSI) classification benefits from effectively handling both spectral and spatial features. However, deep learning (DL) models, like graph convolutional networks (GCN), face challenges in computation time, overfitting, and less informative features. To address these challenges, we propose a novel method called MV-GRL (multi-view graph representation learning) for HSI classification using spectral-spatial graph neural networks. MV-GRL incorporates spectral and spatial features with the extended morphological profile (EMP) and employs a multi-view graph autoencoder (MVGAE) to learn a low-dimensional latent graph representation by fusing two input graphs: spectral features-based graph and spatial-based graph. This fusion enhances the discriminative power of the features. Additionally, we introduce a semi-supervised spectral-spatial GCN using the multi-view latent representation, leveraging labeled and unlabeled samples for improved classification performance. By leveraging both labeled and unlabeled data, our method effectively captures underlying relationships and enhances overall accuracy. Experimental results on the Indian Pines, Salinas, and Pavia University datasets demonstrate its competitive performance, achieving overall accuracy (OA) scores of 96.91%, 97.64%, and 98.88%, respectively, surpassing state-of-the-art (SOTA) models.

关键词： HSI classification Multi-view graph representation learning graph autoencoder graph convolutional networks

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graph Neural Network-Based Approach for Detecting False Data Injection Attacks on Voltage Stability

IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY

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IEEE OPEN ACCESS JOURNAL OF POWER AND ENERGY 2025年 12卷 12-23页

作者： Fahim, Shahriar Rahman Atat, Rachad Kececi, Cihat Takiddin, Abdulrahman Ismail, Muhammad Davis, Katherine R. Serpedin, Erchin Texas A&M Univ Elect & Comp Engn Dept College Stn TX 77843 USA Lebanese Amer Univ Dept Comp Sci & Math Beirut 03797751 Lebanon Florida State Univ Dept Elect & Comp Engn Tallahassee FL 32310 USA Tennessee Technol Univ Dept Comp Sci Cookeville TN 38505 USA

The integration of information and communication technologies into modern power systems has contributed to enhanced efficiency, controllability, and voltage regulation. Concurrently, these technologies expose power systems to cyberattacks, which could lead to voltage instability and significant damage. Traditional false data injection attacks (FDIAs) detectors are inadequate in addressing cyberattacks on voltage regulation since a) they overlook such attacks within power grids and b) primarily rely on static thresholds and simple anomaly detection techniques, which cannot capture the complex interplay between voltage stability, cyberattacks, and defensive actions. To address the aforementioned challenges, this paper develops an FDIA detection approach that considers data falsification attacks on voltage regulation and enhances the voltage stability index. A graph autoencoder-based detector that is able to identify cyberattacks targeting voltage regulation is proposed. A bi-level optimization approach is put forward to concurrently optimize the objectives of both attackers and defenders in the context of voltage regulation. The proposed detector underwent rigorous training and testing across different kinds of attacks, demonstrating enhanced generalization performance in all situations. Simulations were performed on the Iberian power system topology, featuring 486 buses. The proposed model achieves 98.11% average detection rate, which represents a significant enhancement of 10-25% compared to the cutting-edge detectors. This provides strong evidence for the effectiveness of proposed strategy in tackling cyberattacks on voltage regulation.

关键词： Power system stability Voltage control Stability criteria Detectors Computer crime Indexes Reactive power autoencoders Voltage measurement Power grids Cybersecurity voltage regulation graph autoencoder voltage stability false data injection attacks bad data intrusion machine learning

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Fusion of multi-source relationships and topology to infer lncRNA-protein interactions

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MOLECULAR THERAPY NUCLEIC ACIDS 2024年第2期35卷 102187页

作者： Zhang, Xinyu Liu, Mingzhe Li, Zhen Zhuo, Linlin Fu, Xiangzheng Zou, Quan Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325027 Peoples R China Guangzhou Univ Inst Computat Sci & Technol Guangzhou 510000 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410012 Peoples R China Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu 611730 Peoples R China

Long non -coding RNAs (lncRNAs) are important factors involved in biological regulatory networks. Accurately predicting lncRNA-protein interactions (LPIs) is vital for clarifying lncRNA ' s functions and pathogenic mechanisms. Existing deep learning models have yet to yield satisfactory results in LPI prediction. Recently, graph autoencoders (GAEs) have seen rapid development, excelling in tasks like link prediction and node classi fi cation. We employed GAE technology for LPI prediction, devising the FMSRT-LPI model based on path masking and degree regression strategies and thereby achieving satisfactory outcomes. This represents the fi rst known integration of path masking and degree regression strategies into the GAE framework for potential LPI inference. The effectiveness of our FMSRT-LPI model primarily relies on four key aspects. First, within the GAE framework, our model integrates multi -source relationships of lncRNAs and proteins with LPN ' s topological data. Second, the implemented masking strategy ef fi ciently identi fi es LPN ' s key paths, reconstructs the network, and reduces the impact of redundant or incorrect data. Third, the integrated degree decoder balances degree and structural information, enhancing node representation. Fourth, the PolyLoss function we introduced is more appropriate for LPI prediction tasks. The results on multiple public datasets further demonstrate our model ' s potential in LPI prediction.

关键词： MT: Bioinformatics lncRNA-protein interactions LPI graph autoencoder GAE path masking strategy multi-source relationships degree decoder PolyLoss function

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MSLTE: multiple self-supervised learning tasks for enhancing EEG emotion recognition

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JOURNAL OF NEURAL ENGINEERING 2024年第2期21卷 024003-024003页

作者： Li, Guangqiang Chen, Ning Niu, Yixiang Xu, Zhangyong Dong, Yuxuan Jin, Jing Zhu, Hongqin East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

Objective. The instability of the EEG acquisition devices may lead to information loss in the channels or frequency bands of the collected EEG. This phenomenon may be ignored in available models, which leads to the overfitting and low generalization of the model. Approach. Multiple self-supervised learning tasks are introduced in the proposed model to enhance the generalization of EEG emotion recognition and reduce the overfitting problem to some extent. Firstly, channel masking and frequency masking are introduced to simulate the information loss in certain channels and frequency bands resulting from the instability of EEG, and two self-supervised learning-based feature reconstruction tasks combining masked graph autoencoders (GAE) are constructed to enhance the generalization of the shared encoder. Secondly, to take full advantage of the complementary information contained in these two self-supervised learning tasks to ensure the reliability of feature reconstruction, a weight sharing (WS) mechanism is introduced between the two graph decoders. Thirdly, an adaptive weight multi-task loss (AWML) strategy based on homoscedastic uncertainty is adopted to combine the supervised learning loss and the two self-supervised learning losses to enhance the performance further. Main results. Experimental results on SEED, SEED-V, and DEAP datasets demonstrate that: (i) Generally, the proposed model achieves higher averaged emotion classification accuracy than various baselines included in both subject-dependent and subject-independent scenarios. (ii) Each key module contributes to the performance enhancement of the proposed model. (iii) It achieves higher training efficiency, and significantly lower model size and computational complexity than the state-of-the-art (SOTA) multi-task-based model. (iv) The performances of the proposed model are less influenced by the key parameters. Significance. The introduction of the self-supervised learning task helps to enhance the generaliz

关键词： EEG emotion recognition mask-based self-supervised learning multi-task learning graph autoencoder weight sharing

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Predicting abiotic stress-responsive miRNA in plants based on multi-source features fusion and graph neural network

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PLANT METHODS 2024年第1期20卷 33页

作者： Chang, Liming Jin, Xiu Rao, Yuan Zhang, Xiaodan Anhui Agr Univ Coll Informat & Artificial Intelligence Hefei 230036 Peoples R China Anhui Agr Univ Anhui Prov Key Lab Smart Agr Technol & Equipment Hefei 230036 Peoples R China

BackgroundMore and more studies show that miRNA plays a crucial role in plants' response to different abiotic stresses. However, traditional experimental methods are often expensive and inefficient, so it is important to develop efficient and economical computational methods. Although researchers have developed machine learning-based method, the information of miRNAs and abiotic stresses has not been fully exploited. Therefore, we propose a novel approach based on graph neural networks for predicting potential miRNA-abiotic stress *** this study, we fully considered the multi-source feature information from miRNAs and abiotic stresses, and calculated and integrated the similarity network of miRNA and abiotic stress from different feature perspectives using multiple similarity measures. Then, the above multi-source similarity network and association information between miRNAs and abiotic stresses are effectively fused through heterogeneous networks. Subsequently, the Restart Random Walk (RWR) algorithm is employed to extract global structural information from heterogeneous networks, providing feature vectors for miRNA and abiotic stress. After that, we utilized the graph autoencoder based on GIN (graph Isomorphism Networks) to learn and reconstruct a miRNA-abiotic stress association matrix to obtain potential miRNA-abiotic stress associations. The experimental results show that our model is superior to all known methods in predicting potential miRNA-abiotic stress associations, and the AUPR and AUC metrics of our model achieve 98.24% and 97.43%, respectively, under five-fold *** robustness and effectiveness of our proposed model position it as a valuable approach for advancing the field of miRNA-abiotic stress association prediction. We propose an innovative approach based on multi-source similarity network fusion and graph autoencoder for predicting potential miRNA-abiotic stress *** pioneer the application

关键词： miRNA-abiotic stress association graph neural network Multi-source features graph autoencoder

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Predicting microRNA-Disease Associations Through Multi-View Feature Fusion and Matrix Completion on graph Convolutional Networks

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2025年第1期22卷 151-159页

作者： Ding, Shifei Cao, Chuangui Sun, Fan Li, Xuan Wu, Xindong China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Peoples R China Minist Educ Mine Digitizat Engn Res Ctr Xuzhou 221116 Peoples R China Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ China Hefei 230009 Peoples R China

Since the regulatory roles of microRNAs in complex human diseases have been gradually demonstrated, more and more enlightening models were developed for predicting microRNA-disease associations. These models can inform bio-logical studies on the differential expression of microRNAs. In this paper, a graph convolutional network-based model using multi-view feature fusion and matrix completion, FMCGCN for brevity, is proposed. First of all, graph autoencoders are used to learn multiple embeddings from different networks. For convenience, attention mechanisms are used to fuse them. As a result, multi-view features constructed from different perspectives are aggregated. Then, the fused embedding is fed into the graph convolutional network to aggregate local information. This fused embedding is thought to facilitate feature extraction for graph convolutional networks. Finally, feature and nuclear norm minimization, a method for matrix completion, is used to obtain the prediction matrix. Additionally, evaluation results under 5-fold cross-validation prove that FMCGCN outperforms current models in several metrics. Furthermore, case studies for esophageal neoplasms and pancreatic neoplasms also demonstrate the validity of our model.

关键词： Diseases Semantics Predictive models Kernel Topology graph convolutional networks Feature extraction Learning systems Biological system modeling Reliability Feature and nuclear norm minimization graph autoencoder graph convolutional network matrix completion MicroRNA-disease association prediction

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Dual-Network Cross-Learning for Metabolite-Disease Association Prediction

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2025年第2期22卷 545-556页

作者： Chen, Yanxin Ning, Qiao Zhang, Yitong Li, Hui Guo, Shikai Dalian Maritime Univ Dept Informat Sci & Technol Dalian 116026 Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China Dalian Key Lab Artificial Intelligence Dalian 116026 Peoples R China

In recent years, increasing evidence has demonstrated a close association between metabolites and various complex human diseases, providing valuable insights for disease diagnosis, treatment, and prevention. Although deep learning-based approaches have achieved certain success in predicting metabolic disease associations, challenges remain in enriching graph information and effectively integrating metabolic and disease features. To address these issues, this paper proposes a model named DCMDA, which extracts deep features of both metabolites and diseases using Dual-network Cross-learning for Metabolite-Disease Association prediction. DCMDA consists of three parts. The data processing module integrates similarity networks with association networks to construct a heterogeneous network. The feature extraction module extracts features from the metabolite-disease association network based on the non-negative matrix factorization method and from the heterogeneous network using graph autoencoder techniques. The feature fusion module combines the association matrix feature with the heterogeneous network feature through a Cross-Attention mechanism, thereby obtaining deep representations of metabolites and diseases. These features are then used to train the model to predict association scores between metabolites and diseases. Experimental results demonstrate that in 5-fold cross-validation, DCMDA achieves an area under the receiver operating characteristic curve (AUC) of 97.8% and an area under the precision-recall curve (AUPR) of 97.9%, outperforming state-of-the-art prediction methods.

关键词： Diseases Feature extraction Data mining Semantics Deep learning Kernel Information entropy Prediction algorithms Noise Nearest neighbor methods Metabolite-disease association graph autoencoder non-negative matrix factorization Cross-Attention mechanism

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Unsupervised graph Embedding via Adaptive graph Learning

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第4期45卷 5329-5336页

作者： Zhang, Rui Zhang, Yunxing Lu, Chengjun Li, Xuelong Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Peoples R China

graph autoencoders (GAEs) are powerful tools in representation learning for graph embedding. However, the performance of GAEs is very dependent on the quality of the graph structure, i.e., of the adjacency matrix. In other words, GAEs would perform poorly when the adjacency matrix is incomplete or be disturbed. In this paper, two novel unsupervised graph embedding methods, unsupervised graph embedding via adaptive graph learning (BAGE) and unsupervised graph embedding via variational adaptive graph learning (VBAGE) are proposed. The proposed methods expand the application range of GAEs on graph embedding, i.e, on the general datasets without graph structure. Meanwhile, the adaptive learning mechanism can initialize the adjacency matrix without being affected by the parameter. Besides that, the latent representations are embedded with the Laplacian graph structure to preserve the topology structure of the graph in the vector space. Moreover, the adjacency matrix can be self-learned for better embedding performance when the original graph structure is incomplete. With adaptive learning, the proposed method is much more robust to the graph structure. Experimental studies on several datasets validate our design and demonstrate that our methods outperform baselines by a wide margin in node clustering, node classification, link prediction, and graph visualization tasks.

关键词： Laplace equations graph neural networks Adaptation models Adaptive learning Task analysis Decoding Topology graph embedding adaptive graph learning graph autoencoder

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