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An unsupervised false data detection method based on graph autoencoder and attention network in power grid

ELECTRICAL ENGINEERING 2025年第1期107卷 869-881页

作者： Yang, Yingjie Cai, Tiantian Liu, Dehong Li, Xueping Wang, Yaokun Lu, Zhigang China Southern Power Grid Digital Grid Res Inst Co Guangzhou 510670 Guangdong Peoples R China Yanshan Univ Key Lab Power Elect Energy Conservat & Motor Dr He Qinhuangdao 066004 Hebei Peoples R China

False Data Injection Attacks (FDIAs) can bypass traditional state estimation detection, which threatens the security of power systems. Data-driven detection is an effective method for detecting FDIAs. However, for supervised detection methods, it is difficult to obtain a large number of anomaly labels in the power system. To address the issue of insufficient anomaly labels, this paper proposes an unsupervised FDIAs detection method based on graph autoencoder and graph attention neural network (GAE-GAT). In the method, the GAE aggregates the characteristics of topology and operation data in power system. To improve node representation, attention mechanism is adopted to aggregate adjacent node weights. This method is tested on the IEEE-14 and 118 bus systems for three scenarios: static topology, dynamic topology, and renewable energy integration. The results demonstrate that compared with previous unsupervised detection methods, the proposed method can greatly improve detection accuracy.

关键词： Unsupervised false data detection Power system security graph autoencoder graph attention neural network

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Spatial-temporal masked graph autoencoder for rotating machinery fault diagnosis with limited data

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JOURNAL OF VIBRATION AND CONTROL 2025年

作者： Xiong, Wanquan Han, Yanfeng Wu, Qile Xiao, Ke Song, Bin Chongqing Univ State Key Lab Mech Transmiss Adv Equipments Chongqing 400044 Peoples R China ROKAE Beijing Technol Co Ltd Technol Ctr Beijing Peoples R China

Due to the difficulty and high cost associated with obtaining samples of faults in rotating machinery, current intelligent fault diagnosis methods struggle to extract sufficiently diverse fault features, leading to poor diagnostic performance. Additionally, existing deep learning-based approaches often focus solely on extracting temporal or spatial information from sensor signals, ignoring spatial-temporal correlations. Addressing these issues, a novel spatial-temporal masked graph autoencoder (STMGAE) framework is proposed for fault diagnosis in rotating machinery with limited data. In this proposed methodology, a masked strategy is applied to graph-structured data constructed from multi-sensor signals, enabling the model to enhance feature learning capability by training on incomplete graph-structured data masked accordingly. Furthermore, a spatial-temporal graph attention encoder module is introduced to capture temporal and spatial dependencies. To effectively reconstruct the masked portions of the graph-structured data, a correlation similarity decoder is designed, which achieves the desired outcome by capturing correlation similarity between nodes at different granularities, thus improving the model's performance. Experimental validation of STMGAE's effectiveness in fault diagnosis with limited data is conducted on two publicly available datasets, demonstrating superior performance compared to existing methods.

关键词： fault diagnosis limited data masked strategy graph autoencoder rotating machinery

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Pre-training graph autoencoder incorporating hierarchical topology knowledge

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 265卷

作者： Zhu, Hongyin Li, Yakun Liu, Luyang Tong, Haonan Zhang, Chuang Lin, Qunyang 360 Search Dept Qihoo 360 Beijing Peoples R China Beijing Forestry Univ Sch Informat Sci & Technol Haidian East Rd Beijing 100083 Peoples R China Inspur Elect Informat Ind Co Ltd Architecture Res Dept Beijing Peoples R China Natl Key Lab High end Server Syst Jinan Peoples R China

Existing graph pre-training methods demonstrate their ability to generate vertex representations beneficial for downstream machine-learning tasks. However, the quality of these representations is often influenced by the choice of learning algorithm. While masking strategies are commonly employed, random masks can lead to noisy neighborhoods and incomplete graph topology, hampering learning efficiency and increasing computational costs. When a significant portion of neighbors are randomly masked, the central vertex lacks sufficient contextual information. To address this challenge, we integrate hierarchical topology knowledge to enhance masking strategies, thereby preserving the major topology and minimizing training costs. Our method leverages 3 distinct masking techniques: global-aware, local-aware, and element-aware masking. Global-aware masking encourages the model to capture the graph's overall topology, while local-aware masking focuses on capturing vertex interactions. Element-aware masking enhances the model's robustness against noise and structural variations. By incorporating these 3 strategies, our method mitigates the impact of random noise and structural variations during training, yielding more robust and effective vertex representations. To further optimize the model, we introduce a fine-grained subgraph regularization, which reduces the model's parameter count by penalizing divergence in subgraph embeddings across multiple views. We assess the effectiveness of our approach on 3 datasets, highlighting its performance improvements while achieving a reduction of 25% in model parameters.

关键词： graph pre-training Hierarchical topology knowledge graph autoencoder Subgraph regularization

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graph autoencoder for directed weighted network

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SOFT COMPUTING 2022年第3期26卷 1217-1230页

作者： Ma, Yang Li, Yan Liang, Xingxing Cheng, Guangquan Feng, Yanghe Liu, Zhong Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China

Network embedding technology transforms network structure into node vectors, which reduces the complexity of representation and can be effectively applied to tasks such as classification, network reconstruction and link prediction. The main concern of network embedding is to keep the local structural features while effectively capturing the global features of the network. The "shallow" network representation models cannot capture the deep nonlinear features of the network, and the generated network embedding is usually not the optimal solution. In this paper, a new graph autoencoder-based network representation model combines the first- and second-order proximity to evaluate the performance of network embedding. Aiming at the shortcomings of existing network representation methods in weighted and directed networks, on one hand, the concepts of receiving vector and sending vector are introduced with a simplification of decoding part of the neural network which reduces computation complexity;on the other hand, a measurement index based on node degree is proposed to better emphasize the weighted information in the application of network representation. Experiments including directed weighted networks and undirected unweighted networks show that the proposed method achieves better results than the baseline methods for network reconstruction and link prediction tasks and is of higher computation efficiency than previous graph autoencoder algorithms. Besides, the proposed weighted index is able to improve performances of all baseline methods as an external assistance.

关键词： Complex network Network embedding Network reconstruction Link prediction graph autoencoder

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graph autoencoder-BASED EMBEDDED LEARNING IN DYNAMIC BRAIN NETWORKS FOR AUTISM SPECTRUM DISORDER IDENTIFICATION 29

GRAPH AUTOENCODER-BASED EMBEDDED LEARNING IN DYNAMIC BRAIN N...

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IEEE International Conference on Image Processing (ICIP)

作者： Noman, Fuad Yap, Sin-Yee Phan, Raphael C. -W. Ombao, Hernando Ting, Chee-Ming Monash Univ Sch Informat Technol Malaysia campus Subang Jaya Malaysia King Abdullah Univ Sci & Technol Stat Program Thuwal Saudi Arabia

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Recent applications of pattern recognition techniques to brain connectome-based classification focus on static functional connectivity (FC) neglecting the dynamics of FC over time, and use input connectivity matrices on a regular Euclidean grid. We exploit the graph convolutional networks (GCNs) to learn irregular structural patterns in brain FC networks and propose extensions to capture dynamic changes in network topology. We develop a dynamic graph autoencoder (DyGAE)-based framework to leverage the time-varying topological structures of dynamic brain networks for identification of autism spectrum disorder (ASD). The framework combines a GCN-based DyGAE to encode individual-level dynamic networks into time-varying low-dimensional network embeddings, and classifiers based on weighted fully-connected neural network (FCNN) and long short-term memory (LSTM) to facilitate dynamic graph classification via the learned spatial-temporal information. Evaluation on a large ABIDE resting-state functional magnetic resonance imaging (rs-fMRI) dataset shows that our method outperformed state-of-the-art methods in detecting altered FC in ASD. Dynamic FC analyses with DyGAE learned embeddings also reveal apparent group difference between ASD and healthy controls in network profiles and switching dynamics of brain states.

关键词： Brain connectivity networks graph autoencoder graph convolutional network resting-state fMRI

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GAEBic:A Novel Biclustering Analysis Method for miRNA-Targeted Gene Data Based on graph autoencoder

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Journal of Computer Science & Technology 2021年第2期36卷 299-309页

作者： Li Wang Hao Zhang Hao-Wu Chang Qing-Ming Qin Bo-Rui Zhang Xue-Qing Li Tian-Heng Zhao Tian-Yue Zhang College of Software Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China College of Plant Science Jilin UniversityChangchun 130062China Department of Biochemistry University of Illinois at Urbana-ChampaignChampaign 61820U.S.A

Unlike traditional clustering analysis,the biclustering algorithm works simultaneously on two dimensions of samples(row)and variables(column).In recent years,biclustering methods have been developed rapidly and widely applied in biological data analysis,text clustering,recommendation system and other *** traditional clustering algorithms cannot be well adapted to process high-dimensional data and/or large-scale *** present,most of the biclustering algorithms are designed for the differentially expressed big biological ***,there is little discussion on binary data clustering mining such as miRNA-targeted gene ***,we propose a novel biclustering method for miRNA-targeted gene data based on graph autoencoder named as *** applies graph autoencoder to capture the similarity of sample sets or variable sets,and takes a new irregular clustering strategy to mine biclusters with excellent *** on the miRNA-targeted gene data of soybean,we benchmark several different types of the biclustering algorithm,and find that GAEBic performs better than Bimax,Bibit and the Spectral Biclustering algorithm in terms of target gene *** biclustering method achieves comparable performance on the high throughput miRNA data of soybean and it can also be used for other species.

关键词： biclustering graph autoencoder miRNA-targeted gene binary data

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A noise injection strategy for graph autoencoder training

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NEURAL COMPUTING & APPLICATIONS 2021年第10期33卷 4807-4814页

作者： Wang, Yingfeng Xu, Biyun Kwak, Myungjae Zeng, Xiaoqin Middle Georgia State Univ Dept Informat Technol Macon GA 31206 USA Beijing Kubao Technol Co Beijing 100124 Peoples R China Hohai Univ Inst Intelligence Sci & Technol Nanjing 210098 Jiangsu Peoples R China Univ Tennessee Dept Comp Sci & Engn Chattanooga TN 37403 USA

graph autoencoder can map graph data into a low-dimensional space. It is a powerful graph embedding method applied in graph analytics to lower the computational cost. Researchers have developed different graph autoencoders for addressing different needs. This paper proposes a strategy based on noise injection for graph autoencoder training. This is a general training strategy that can flexibly fit most existing training algorithms. The experimental results verify this general strategy can significantly reduce overfitting and identify the noise rate setting for consistent training performance improvement.

关键词： graph autoencoder Noise injection Training algorithm Overfitting

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CDA-SKAG: Predicting circRNA-disease associations using similarity kernel fusion and an attention-enhancing graph autoencoder

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第5期20卷 7957-7980页

作者： Wang, Huiqing Han, Jiale Li, Haolin Duan, Liguo Liu, Zhihao Cheng, Hao Taiyuan Univ Technol Coll Informat & Comp Taiyuan 030024 Peoples R China

Circular RNAs (circRNAs) constitute a category of circular non-coding RNA molecules whose abnormal expression is closely associated with the development of diseases. As biological data become abundant, a lot of computational prediction models have been used for circRNA-disease association prediction. However, existing prediction models ignore the non-linear information of circRNAs and diseases when fusing multi-source similarities. In addition, these models fail to take full advantage of the vital feature information of high-similarity neighbor nodes when extracting features of circRNAs or diseases. In this paper, we propose a deep learning model, CDA-SKAG, which introduces a similarity kernel fusion algorithm to integrate multi-source similarity matrices to capture the non-linear information of circRNAs or diseases, and construct a circRNA information space and a disease information space. The model embeds an attention-enhancing layer in the graph autoencoder to enhance the associations between nodes with higher similarity. A cost-sensitive neural network is introduced to address the problem of positive and negative sample imbalance, consequently improving our model's generalization capability. The experimental results show that the prediction performance of our model CDA-SKAG outperformed existing circRNA-disease association prediction models. The results of the case studies on lung and cervical cancer suggest that CDA-SKAG can be utilized as an effective tool to assist in predicting circRNA-disease associations.

关键词： circRNA-disease association prediction similarity kernel fusion graph autoencoder attention mechanism cost-sensitive neural network

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SIGA: social influence modeling integrating graph autoencoder for rating prediction

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APPLIED INTELLIGENCE 2023年第6期53卷 6432-6447页

作者： Liu, Jinxin Xiao, Yingyuan Zheng, Wenguang Hsu, Ching-Hsien Tianjin Univ Technol Engn Res Ctr Learning Based Intelligent Syst Minist Educ Tianjin 300384 Peoples R China Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Novel Softwar Tianjin 300384 Peoples R China Asia Univ Coll Informat & Elect Engn Taichung 41354 Taiwan

With the revival of social networks, many studies try to integrate social relations of users to improve the accuracy of rating prediction. However, most existing methods cannot accurately reflect how social relations affect user preferences. The main reason is that these methods only investigate the directly or indirectly reachable nodes in social networks, while ignoring global transitivity of influence. Actually, for a particular user, his preference will not only affect his neighbors but also some people he does not know, especially when the person is an opinion leader. In this paper, we propose a social influence model, SIGA, which integrates graph autoencoder and is used for the rating prediction task. First, we establish a new method to quantify the social influence of users from the perspective of information dissemination in social networks. Second, we employ graph autoencoder (GAE) to model the interaction between users and items from the view of message passing on bipartite graph, which can obtain high-quality representations of users and items. By building a hybrid architecture of social modeling and GAE, it is expected to be endowed with both benefits from them. In addition, our model is interpretable at both the structural level and attribute level. Extensive experimental results on four real-world datasets have shown the effectiveness and generalization of the proposed model.

关键词： Social influence Recommendation system Rating prediction graph autoencoder

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VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder

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BMC BIOINFORMATICS 2023年第1期24卷 1-17页

作者： Zhang, Yuanyuan Feng, Yinfei Wu, Mengjie Deng, Zengqian Wang, Shudong Yinfei Feng Qingdao Univ Technol Qingdao Peoples R China China Univ Petr Sch Comp Sci & Technol Qingdao Peoples R China

MotivationAccurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing. (i) Traditional methods do not consider the use of multi-source data and do not consider the complex relationship between data sources. (ii) How to better mine the hidden features of drug and target space from high-dimensional data, and better solve the accuracy and robustness of the *** solve the above problems, a novel prediction model named VGAEDTI is proposed in this paper. We constructed a heterogeneous network with multiple sources of information using multiple types of drug and target dataIn order to obtain deeper features of drugs and targets, we use two different autoencoders. One is variational graph autoencoder (VGAE) which is used to infer feature representations from drug and target spaces. The second is graph autoencoder (GAE) propagating labels between known DTIs. Experimental results on two public datasets show that the prediction accuracy of VGAEDTI is better than that of six DTIs prediction methods. These results indicate that model can predict new DTIs and provide an effective tool for accelerating drug development and repurposing.

关键词： Drug-target interaction prediction Variational inference graph autoencoder Variational expected maximum algorithm Drug repurposing

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