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graph-structured data generation and analysis for anomaly detection in an automated manufacturing process

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2024年第10期38卷 5617-5625页

作者： Kim, Namki Gao, Xinpu Yang, Jeongsam UDMTEK Co Ltd X AI Team 256 Changryong Daero Suwon 16229 Gyeonggi Do South Korea Ajou Univ Dept Ind Engn 206 Worldcup Ro Suwon 16499 Gyeonggi Do South Korea

During automated manufacturing processes, multiple sensors are attached to facilities to collect and analyze analog data for detecting operational anomalies. However, owing to facility devices being interlinked by a control system, simultaneous examination of the control system and analog data enhances the accuracy of anomaly detection and diagnosis of root causes. We proposed a system detecting anomalies by integrating an internal control system with external analog data and representing it in a graph structure. The system generates and combines the adjacency and feature matrices for training a convolutional autoencoder model to identify operational anomalies. Performance tests revealed distinct operational patterns in the cycle data flagged by the model as anomalies. The system diagnosed the root cause of anomalies, such as control operation sequencing, timing variances, and shifts in analog or video signals. This approach may enhance the productivity and quality of the manufacturing processes by facilitating anomaly detection and cause diagnosis.

关键词： Adjacency matrix Anomaly detection Automated manufacturing process Feature extraction graph-structured data

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Preserving the Privacy of Latent Information for graph-structured data

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2023年 18卷 5041-5055页

作者： Shan, Baoling Yuan, Xin Ni, Wei Wang, Xin Liu, Ren Ping Dutkiewicz, Eryk Univ Technol Sydney Global Big Data Technol Ctr Ultimo NSW 2007 Australia CSIRO Data61 Marsfield NSW 2122 Australia Fudan Univ Dept Commun Sci & Engn Shanghai 200433 Peoples R China

Latent graph structure and stimulus of graph-structured data contain critical private information, such as brain disorders in functional magnetic resonance imaging data, and can be exploited to identify individuals. It is critical to perturb the latent information while maintaining the utility of the data, which, unfortunately, has never been addressed. This paper presents a novel approach to obfuscating the latent information and maximizing the utility. Specifically, we first analyze the graph Fourier transform (GFT) basis that captures the latent graph structures, and the latent stimuli that are the spectral-domain inputs to the latent graphs. Then, we formulate and decouple a new multi-objective problem to alternately obfuscate the GFT basis and stimuli. The difference-of-convex (DC) programming and Stiefel manifold gradient descent are orchestrated to obfuscate the GFT basis. The DC programming and gradient descent are employed to perturb the spectral-domain stimuli. Experiments conducted on an attention-deficit hyperactivity disorder dataset demonstrate that our approach can substantially outperform its differential privacy-based benchmark in the face of the latest graph inference attacks.

关键词： graph-structured data latent information privacy graph fourier transform (GFT)

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Managing Change in graph-structured data Using Description Logics

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ACM TRANSACTIONS ON COMPUTATIONAL LOGIC 2017年第4期18卷 1–35页

作者： Ahmetaj, Shqiponja Calvanese, Diego Ortiz, Magdalena Simkus, Mantas Tech Univ Wien Inst Informat Syst 184 3 Favoritenstr 9-11 A-1040 Vienna Austria Free Univ Bozen Bolzano Fac Comp Sci Piazza Domenicani 3 I-39100 Bolzano Italy

In this article, we consider the setting of graph-structured data that evolves as a result of operations carried out by users or applications. We study different reasoning problems, which range from deciding whether a given sequence of actions preserves the satisfaction of a given set of integrity constraints, for every possible initial data instance, to deciding the (non) existence of a sequence of actions that would take the data to an (un)desirable state, starting either from a specific data instance or from an incomplete description of it. For describing states of the data instances and expressing integrity constraints on them, we use description logics (DLs) closely related to the two-variable fragment of first-order logic with counting quantifiers. The updates are defined as actions in a simple yet flexible language, as finite sequences of conditional insertions and deletions, which allow one to use complex DL formulas to select the (pairs of) nodes for which (node or arc) labels are added or deleted. We formalize the preceding data management problems as a static verification problem and several planning problems and show that, due to the adequate choice of formalisms for describing actions and states of the data, most of these data management problems can be effectively reduced to the (un)satisfiability of suitable formulas in decidable logical formalisms. Leveraging this, we provide algorithms and tight complexity bounds for the formalized problems, both for expressive DLs and for a variant of the popular DL-Lite, advocated for data management in recent years.

关键词： Integrity constraints graph-structured data static analysis planning

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Privacy-assured substructure similarity query over encrypted graph-structured data in cloud

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SECURITY AND COMMUNICATION NETWORKS 2014年第11期7卷 1933-1944页

作者： Zhang, Yingguang Su, Sen Wang, Yulong Chen, Weifeng Yang, Fangchun Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100088 Peoples R China

In recent years, large amounts of graph-structured data have been outsourced to the commercial public cloud. It is a crucial requirement to enable substructure similarity query for effective data retrieval. However, for protecting data privacy, sensitive data have to be encrypted before outsourcing, which impedes the traditional similarity query schemes from being supported in cloud. Most existing works on encrypted cloud data retrieval pay little attention to this problem. Additionally, considering the huge amounts of encrypted data graphs, the complicated similarity computation and privacy requirements, it is particularly challenging to solve this problem effectively. In this paper, for the first time, we investigate the problem of privacy-assured substructure similarity query over encrypted graph-structured data in cloud computing. Our solution explores a secure framework and a series of secure algorithms to efficiently perform the substructure similarity query without privacy breaches. The proposed solution first builds a secure feature-graph index to represent the feature-related information about each encrypted data graph based on privacy homomorphism and obscuration methods and then calculates the similarity between the query graph and each data graph by the difference of feature frequency in a privacy-preserving manner. Thorough analysis is given to investigate effectiveness and privacy guarantees, and the experiments with real dataset further demonstrate the validity and efficiency of the proposed solution. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： substructure similarity query graph-structured data cloud computing privacy-assured

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Counterfactual Learning on graphs:A Survey

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Machine Intelligence Research 2025年第1期22卷 17-59页

作者： Zhimeng Guo Zongyu Wu Teng Xiao Charu Aggarwal Hui Liu Suhang Wang College of Information Sciences and Technology Pennsylvania State UniversityUniversity Park 16802USA International Business Machines Corporation T.J.Watson Research Center New York 10598USA College of Engineering Michigan State UniversityEast Lansing 48824USA

graph-structured data are pervasive in the real-world such as social networks,molecular graphs and transaction *** neural networks(GNNs)have achieved great success in representation learning on graphs,facilitating various downstream ***,GNNs have several drawbacks such as lacking interpretability,can easily inherit the bias of data and cannot model casual ***,counterfactual learning on graphs has shown promising results in alleviating these *** approaches have been proposed for counterfactual fairness,explainability,link prediction and other applications on *** facilitate the develop-ment of this promising direction,in this survey,we categorize and comprehensively review papers on graph counterfactual *** divide existing methods into four categories based on problems *** each category,we provide background and motivating ex-amples,a general framework summarizing existing works and a detailed review of these *** point out promising future research directions at the intersection of graph-structured data,counterfactual learning,and real-world *** offer a comprehensive view of resources for future studies,we compile a collection of open-source implementations,public datasets,and commonly-used evalu-ation *** survey aims to serve as a“one-stop-shop”for building a unified understanding of graph counterfactual learning cat-egories and current resources.

关键词： Counterfactual learning graph-structured data graph neural networks fairness explainability

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A Multiscale Pooling Attention-Based graph Attention Network for Remaining Useful Life Prediction

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Tang, Jiayin Miao, Yonghao Xia, Yu Zhou, Qiuyang Yi, Cai Southwest Jiaotong Univ Sch Math Dept Stat Chengdu 611756 Sichuan Peoples R China Beihang Univ Sch Reliabil & Syst Engn Beijing 100191 Peoples R China Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst Chengdu 610000 Peoples R China

Owing to the intricate spatial and temporal relationships inherent in data collected from multiple sensors, achieving precise predictions of remaining useful life (RUL) becomes a challenging task. Recently, deep learning (DL)-based approaches have made substantial advancements in RUL prediction. However, the traditional neural network-based methods have encountered some trouble in extracting spatial features. graph neural network (GNN) has demonstrated the ability to effectively capture the spatial dependencies between multisensor data, but current GNN-based approaches cannot achieve much in terms of the spatial-temporal dependencies at various scales. Motivated by this, a multiscale pooling attention-based graph attention network (MSPA-GAT) is proposed. First, a multi-GATv2 network is designed for the spatial dependencies modeling, and a bidirectional long short-term memory (BiLSTM) network is used for modeling the temporal dependencies. Second, a multiscale pooling attention (MSPA) mechanism is constructed to highlight the local details of different scales and capture multilevel information. Finally, the effectiveness of the proposed MSPA-GAT to consider spatial and temporal dependencies is validated using two datasets. Moreover, the experimental results have shown that MSPA-GAT outperforms current state-of-the-art methods in RUL prediction.

关键词： Predictive models Feature extraction data models data mining Mathematical models Bidirectional long short term memory Attention mechanisms graph convolutional networks Fuses Adaptation models graph attention network (GAT) graph-structured data remaining useful life (RUL) prediction

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GPNet: Simplifying graph neural networks via multi-channel geometric polynomials

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INFORMATION SCIENCES 2025年 694卷

作者： Liu, Xun Ng, Alex Hay-Man Lei, Fangyuan Zhang, Yikuan Li, Zhengming Guangdong Univ Technol Sch Informat & Engn Guangzhou Peoples R China Software Engn Inst Guangzhou Dept Elect Guangzhou Peoples R China Guangdong Univ Technol Sch Civil & Transportat Engn Guangzhou Peoples R China Guangdong Polytech Normal Univ Guangdong Prov Key Lab Intellectual Property & Big Guangzhou Peoples R China Guangdong Polytech Normal Univ Sch Cyber Secur Guangzhou Peoples R China

graph neural networks (GNNs) are a promising deep-learning approach for circumventing many real-world problems with graph-structured data. However, these models typically have a minimum of one of the following four fundamental limitations: over-smoothing, over-fitting, difficulty in training, and strong homophily assumptions. For instance, simple graph convolution (SGC) suffers from the first and fourth limitations. To address these limitations, we identified a set of key designs, including (D1) dilated convolution, (D2) multi-channel learning, (D3) self-attention score, and (D4) sign factor, to boost learning from networks of various types ( i.e. homophily and heterophily) and sparsity ( i.e. sparse and dense), and combined them into a GNN, GPNet, which is a simple and efficient one-layer model. We analyzed the model theoretically and demonstrated that it can approximate various graph filters by adjusting the self-attention score and sign factor. Experiments show that GPNet consistently outperforms baselines in terms of average rank, average accuracy, complexity, and parameters on semi-supervised and fully supervised tasks, and achieves performance comparable to state-of-the-art models with inductive learning tasks.

关键词： graph neural networks graph-structured data Arbitrary graph filters Geometric polynomials

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An improved graph convolutional networks for fault diagnosis of rolling bearing with limited labeled data

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第12期34卷 125109-125109页

作者： Xiao, Xiangqu Li, Chaoshun Huang, Jie Yu, Tian Wong, Pak Kin Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Univ Macau Dept Electromech Engn Macau Peoples R China

Rolling bearings are essential parts of rotating equipment. Due to their unique operating environment, bearings are vulnerable to failure. graph neural network (GNN) provides an effective way of mining relationships between data samples. However, various existing GNN models suffer from issues like poor graph-structured data quality and high computational consumption. Moreover, the available fault samples are typically insufficient in real practice. Therefore, an improved graph convolutional network (GCN) is proposed for bearing fault diagnosis with limited labeled data. This method consists of two steps: graph structure data acquisition and improved graph convolution network building. Defining edge failure thresholds simplifies the generated weighted graph-structured data, thereby enhancing data quality and reducing training computation costs. Improvements to standard GCNs can effectively aggregate data features of different receptive field sizes without noticeably raising the computational complexity of the model. Experiments with limited labeled data are conducted on two public datasets and an actual experimental platform dataset to verify the superiority of the proposed method. In addition, experiments on imbalanced datasets also fully demonstrate the robustness of the proposed method.

关键词： fault diagnosis improved graph convolutional network graph-structured data limited labeled data rolling bearing

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A hyperbolic approach for learning communities on graphs

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data MINING AND KNOWLEDGE DISCOVERY 2023年第3期37卷 1090-1124页

作者： Gerald, Thomas Zaatiti, Hadi Hajri, Hatem Baskiotis, Nicolas Schwander, Olivier Sci Sorbonne Univ Lip6 F-75005 Paris France Inst Res & Technol SystemX 8 Ave Vauve F-91120 Palaiseau France

Detecting communities on graphs has received significant interest in recent literature. Current state-of-the-art approaches tackle this problem by coupling Euclidean graph embedding with community detection. Considering the success of hyperbolic representations of graph-structured data in the last years, an ongoing challenge is to set up a hyperbolic approach to the community detection problem. The present paper meets this challenge by introducing a Riemannian geometry based framework for learning communities on graphs. The proposed methodology combines graph embedding on hyperbolic spaces with Riemannian K-means or Riemannian mixture models to perform community detection. The usefulness of this framework is illustrated through several experiments on generated community graphs and real-world social networks as well as comparisons with the most powerful baselines.

关键词： Manifolds Representation learning graph-structured data Hyperbolic space Community embedding

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Efficient Non-Sequential Relational Modeling for Temporal Knowledge graph Link Predictions

Efficient Non-Sequential Relational Modeling for Temporal Kn...

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

作者： Dong, Linhua Zhou, Xiaofei Zhu, Qiannan Bai, Xu Xiong, Gang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence Beijing Normal University Beijing China

ISBN: (纸本)9798350368741

Temporal Knowledge graphs (TKGs) are being widely explored to predict the future for they record multi-relational knowledge and the happening time of real-life facts. Existing works learn sequential patterns to infer the future from past facts in TKGs for predictions. Although achieving promising results, they are restricted by the sequential modeling in both efficiency and effectiveness. To resolve these limitations, we propose our efficient and effective non-sequential relational modeling (NoSeq). NoSeq works non-sequentially for temporal patterns where it transforms the happening time into time intervals. Time intervals are the period of time between happening time and prediction time which state the temporal distance clearly. Both time intervals and relations are represented using embeddings and merged non-sequentially into entity embeddings for future predictions. We evaluate NoSeq on four datasets from the perspective of effectiveness, efficiency, sensitivity, and the ability to transfer. Consistent better performances verify our idea. © 2025 IEEE.

关键词： graph-structured data Temporal Knowledge graph Temporal Knowledge graph Link Predictions

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