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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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High-Speed Adder Design Space Exploration via graph Neural Processes

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2022年第8期41卷 2657-2670页

作者： Geng, Hao Ma, Yuzhe Xu, Qi Miao, Jin Roy, Subhendu Yu, Bei Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Univ Sci & Technol China Sch Microelect Hefei 230052 Peoples R China Google Mountain View CA 94043 USA Cadence Design Syst Design & Sign Grp Machine Learning Grp San Jose CA 95134 USA

Adders are the primary components in the data-path logic of a microprocessor, and thus, adder design has been always a critical issue in the very large-scale integration (VLSI) industry. However, it is infeasible for designers to obtain optimal adder architecture by exhaustively running EDA flow due to the extremely large design space. Previous arts have proposed the machine learning-based framework to explore the design space. Nevertheless, they fall into suboptimality due to a two-stage flow of the learning process and less efficient nor effective feature representations of prefix adder structures. In this article, we first integrate a variational graph autoencoder and a neural process (NP) into an end-to-end, multibranch framework, which is termed the graph neural process. The former performs automatic feature learning of prefix adder structures, whilst the latter one is designed as an alternative to the Gaussian process. Then, we propose a sequential optimization framework with the graph NP as the surrogate model to explore the Pareto-optimal prefix adder structures with tradeoff among Quality-of-Result (QoR) metrics, such as power, area, and delay. The experimental results show that compared with state-of-the-art methodologies, our framework can achieve a much better Pareto frontier in multiple QoR metric spaces with fewer design-flow evaluations.

关键词： Adders Very large scale integration Space exploration Delays Tools Physical design Gaussian processes Design space exploration graph autoencoder graph neural process high speed adder neural process sequential model-based optimization

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Core-GAE: Toward Generation of IoT Networks

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IEEE INTERNET OF THINGS JOURNAL 2022年第12期9卷 9241-9248页

作者： Luo, Qi Yu, Dongxiao Zheng, Yanwei Sheng, Hao Cheng, Xiuzhen Shandong Univ Sch Comp Sci & Technol Qingdao 266237 Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Software Dev Environm Beijing 100191 Peoples R China

To realize simulation experiments in large-scale Internet of Things (IoT) networks, this work studies the utilization of deep graph generative models to generate IoT networks, which can provide an economic approach facilitating IoT to meet the requirements of real-time performance, interoperability, energy efficiency, and coexistence. In IoT, nodes have different attributes, different connection ways with surrounding nodes, and different compactness of the region, which pose great challenges for network generation. By leveraging the properties of k-core and variational autoencoder during network generation, we propose a variable graph autoencoder called Core-GAE incorporating the coreness of nodes. In contrast to previous graph generative models, Core-GAE can preserve the local proximity similarity and maintain the global structural features simultaneously when learning the structural features of graphs. All three of the tasks we experimented with on four data sets show that Core-GAE exhibits better performance than previous ones.

关键词： Core decomposition deep generative model graph autoencoder Internet of Things (IoT) network

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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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Learning graph Embedding With Adversarial Training Methods

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IEEE TRANSACTIONS ON CYBERNETICS 2020年第6期50卷 2475-2487页

作者： Pan, Shirui Hu, Ruiqi Fung, Sai-fu Long, Guodong Jiang, Jing Zhang, Chengqi Monash Univ Fac Informat Technol Clayton Vic 3800 Australia Univ Technol Sydney Fac Engn & Informat Technol Ctr Artificial Intelligence Ultimo NSW 2007 Australia City Univ Hong Kong Dept Appl Social Sci Hong Kong Peoples R China

graph embedding aims to transfer a graph into vectors to facilitate subsequent graph-analytics tasks like link prediction and graph clustering. Most approaches on graph embedding focus on preserving the graph structure or minimizing the reconstruction errors for graph data. They have mostly overlooked the embedding distribution of the latent codes, which unfortunately may lead to inferior representation in many cases. In this article, we present a novel adversarially regularized framework for graph embedding. By employing the graph convolutional network as an encoder, our framework embeds the topological information and node content into a vector representation, from which a graph decoder is further built to reconstruct the input graph. The adversarial training principle is applied to enforce our latent codes to match a prior Gaussian or uniform distribution. Based on this framework, we derive two variants of the adversarial models, the adversarially regularized graph autoencoder (ARGA) and its variational version, and adversarially regularized variational graph autoencoder (ARVGA), to learn the graph embedding effectively. We also exploit other potential variations of ARGA and ARVGA to get a deeper understanding of our designs. Experimental results that compared 12 algorithms for link prediction and 20 algorithms for graph clustering validate our solutions.

关键词： Task analysis Training Clustering algorithms Generators Convolutional codes Decoding Data models Adversarial regularization graph autoencoder graph clustering graph convolutional networks (GCNs) graph embedding link prediction

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Generalized graph Neural Network-Based Detection of False Data Injection Attacks in Smart Grids

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第3期7卷 618-630页

作者： Takiddin, Abdulrahman Atat, Rachad Ismail, Muhammad Boyaci, Osman Davis, Katherine R. Serpedin, Erchin Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Texas A&M Univ Qatar Dept Elect & Comp Engn Doha Qatar Tennessee Technol Univ Dept Comp Sci Cookeville TN 38505 USA

data injection attacks (FDIAs) pose a significant threat to smart power grids. Recent efforts have focused on developing machine learning (ML)-based defense strategies against such attacks. However, existing strategies offer limited detection performance since they (a) lack the capability of embedding the spatial aspects of the power system topology in the detection mechanism, (b) offer topology-specific detection that does not generalize well to practical systems with seasonal reconfigurations in their topology, or (c) offer detection based on only seen types of FDIAs present in the training set. Therefore, in this paper, we aim to develop a defense strategy that offers an improved generalization ability and detection performance against unseen attacks. Towards this objective, we propose a graph autoencoder (GAE)-based detection strategy that (a) captures spatio-temporal features of power systems, hence, offering improved detection performance, (b) is trained on comprehensive graphs reflecting various realizations of power system topologies, hence, offering better generalization abilities, and (c) works effectively against unseen FDIAs. The proposed detector is trained and tested on various topological configurations from 14, 39, and 118-bus systems offering detection rates (DRs) of 93.6%, 95.7%, and 99.1%, respectively, when tested against unseen FDIAs and unseen topologies. This presents an improvement of 11.5 - 30% compared to existing ML-based strategies.

关键词： Detectors Topology Stochastic processes Power systems Training Geometry Data models Cyberattacks false data injection attacks graph autoencoder graph neural network generalized detection machine learning smart power grids

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Deep Learning on graphs: A Survey

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第1期34卷 249-270页

作者： Zhang, Ziwei Cui, Peng Zhu, Wenwu Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. In this survey, we comprehensively review the different types of deep learning methods on graphs. We divide the existing methods into five categories based on their model architectures and training strategies: graph recurrent neural networks, graph convolutional networks, graph autoencoders, graph reinforcement learning, and graph adversarial methods. We then provide a comprehensive overview of these methods in a systematic manner mainly by following their development history. We also analyze the differences and compositions of different methods. Finally, we briefly outline the applications in which they have been used and discuss potential future research directions.

关键词： graph data deep learning graph neural network graph convolutional network graph autoencoder

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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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Multi-channel Partial graph Integration Learning of Partial Multi-omics Data for Cancer Subtyping

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CURRENT BIOINFORMATICS 2023年第8期18卷 680-691页

作者： Cao, Qing-Qing Zhao, Jian-ping Zheng, Chun-Hou Xinjiang Univ Coll Math & Syst Sci Urumqi Peoples R China Anhui Univ Sch Artificial Intelligence Hefei Peoples R China Xinjiang Univ Coll Math & Phys POB 830046 Urumqi Peoples R China Anhui Univ Coll Comp Sci & Technol POB 230039 Hefei Peoples R China

Background The appearance of cancer subtypes with different clinical significance fully reflects the high heterogeneity of cancer. At present, the method of multi-omics integration has become more and more mature. However, in the practical application of the method, the omics of some samples are *** The purpose of this study is to establish a depth model that can effectively integrate and express partial multi-omics data to accurately identify cancer *** We proposed a novel partial multi-omics learning model for cancer subtypes, MPGIL (Multi-channel Partial graph Integration Learning). MPGIL has two main components. Firstly, it obtains more lateral adjacency information between samples within the omics through the multi-channel graph autoencoders based on high-order proximity. To reduce the negative impact of missing samples, the weighted fusion layer is introduced to replace the concatenate layer to learn the consensus representation across multi-omics. Secondly, a classifier is introduced to ensure that the consensus representation is representative of clustering. Finally, subtypes were identified by *** This study compared MPGIL with other multi-omics integration methods on 16 datasets. The clinical and survival results show that MPGIL can effectively identify subtypes. Three ablation experiments are designed to highlight the importance of each component in MPGIL. A case study of AML was conducted. The differentially expressed gene profiles among its subtypes fully reveal the high heterogeneity of *** MPGIL can effectively learn the consistent expression of partial multi-omics datasets and discover subtypes, and shows more significant performance than the state-of-the-art methods.

关键词： Partial multi-omics data high-order proximity cancer data multi-channel classifier graph autoencoder

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Temporal network embedding using graph attention network

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COMPLEX & INTELLIGENT SYSTEMS 2022年第1期8卷 13-27页

作者： Mohan, Anuraj Pramod, K., V Cochin Univ Sci & Technol Dept Comp Applicat Artificial Intelligence Lab Cochin 682022 Kerala India Cochin Univ Sci & Technol Dept Comp Applicat Cochin 682022 Kerala India

graph convolutional network (GCN) has made remarkable progress in learning good representations from graph-structured data. The layer-wise propagation rule of conventional GCN is designed in such a way that the feature aggregation at each node depends on the features of the one-hop neighbouring nodes. Adding an attention layer over the GCN can allow the network to provide different importance within various one-hop neighbours. These methods can capture the properties of static network, but is not well suited to capture the temporal patterns in time-varying networks. In this work, we propose a temporal graph attention network (TempGAN), where the aim is to learn representations from continuous-time temporal network by preserving the temporal proximity between nodes of the network. First, we perform a temporal walk over the network to generate a positive pointwise mutual information matrix (PPMI) which denote the temporal correlation between the nodes. Furthermore, we design a TempGAN architecture which uses both adjacency and PPMI information to generate node embeddings from temporal network. Finally, we conduct link prediction experiments by designing a TempGAN autoencoder to evaluate the quality of the embedding generated, and the results are compared with other state-of-the-art methods.

关键词： Network embedding graph convolution graph autoencoder Temporal networks

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