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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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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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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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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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An Effective Keyword Search Method for graph-structured data Using Extended Answer Structure

An Effective Keyword Search Method for Graph-Structured Data...

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13th International Conference on Computational Science and Its Applications (ICCSA)

作者： Park, Chang-Sup Dongduk Womens Univ Seoul South Korea

ISBN: (纸本)9783642396403

This paper proposes an effective approach to ranked keyword search over graph-structured data which is getting much attraction in various applications. To provide more effective search results than the previous approaches, we suggest an extended answer structure which has no constraint on the number of keyword nodes and is based on a new relevance measure. For efficient keyword search, we also use an inverted list index which pre-computes connectivity and relevance information on the nodes in the graph. We present a query processing algorithm based on the pre-constructed inverted lists, which aggregates entries relevant to each node and finds top-k answer trees relevant to the given query. We also enhance the basic search method by storing additional information on the relevance of the related entries in the lists, in order to estimate the relevance score of each node more closely and to find top-k answers more efficiently. We show by experiments that the proposed keyword search method can provide effective top-k search results over large amount of graph-structured data with good execution performance.

关键词： graph-structured data Keyword Search Top-k Query Processing

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Privacy-Assured Similarity Query over graph-structured data in Mobile Cloud

Privacy-Assured Similarity Query over Graph-Structured Data ...

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33rd IEEE International Conference on Distributed Computing Systems (ICDCS)

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

ISBN: (纸本)9781479932474;9780769550237

In the emerging mobile cloud paradigm, more and more innovative social applications are provided. The data of these applications is usually represented by the graph structure. For effective data retrieval, it is a crucial requirement to enable substructure similarity query over these graph-structured data. The sensitive information of the graphs and users may be leaked during the retrieval, leading to privacy violations. However, most existing works on protecting privacy pay little attention to this problem. Additionally, considering the complicated similarity computation and the privacy requirements, it is particularly challenging to solve the problem effectively. In this paper, for the first time, we investigate the problem of privacy-assured substructure similarity query over graph-structured data in mobile cloud. Our solution proposes a series of secure algorithms to effectively perform the similarity query without privacy breaches. The proposed solution first builds a secure feature-graph index to represent the feature-related information about each 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 privacy and efficiency guarantees, and the experiments further demonstrate the validity and efficiency of the proposed solution.

关键词： Substructure Similarity Query graph-structured data Mobile Cloud Privacy-assured

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An efficient structural index for graph-structured data 08

An efficient structural index for graph-structured data

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7th IEEE/ACIS International Conference on Computer and Information Science in Conjunction with 2nd IEEE/ACIS International Workshop on e-Activity

作者： Fan, Yingjie Zhang, Chenghong Wang, Shuyun Hao, Xiulan Hu, Yunfa Fudan Univ Sch Management Shanghai 200433 Peoples R China Fudan Univ Dept Comp & Informat Technol Shanghai 200433 Peoples R China

ISBN: (纸本)9780769531311

To speed up queries over XML and semi-structured data, a number of structural indexes have been proposed. The structural index is usually a labeled directed graph defined by partitioning nodes in the XML data graph into equivalence classes and storing equivalence classes as index nodes. On the basis of the Inter-Relevant Successive Trees (IRST), we propose an efficient adaptive structural index, IRST(k)-index. Compared with the previous indexes, such as the A (k)-index, D(k)index, and M(k)-index, our experiment results show that the IRST(k)-index performs more efficiently in terms of space consumption and query performance, while using significantly less construction time.

关键词： XML graph-structured data equivalence relation structural summary

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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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