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23rd IEEE International Conference on Data Mining (IEEE ICDM)

作者： Mu, Caihong Huang, Heyuan Fang, Yunfei Liu, Yi Xidian Univ Xian Peoples R China

ISBN: (纸本)9798350307887

graph convolutional neural networks (GCNs) have performed well in many recommendation scenarios. In spite of this, recommendation models based on GCNs still face problems such as insufficient information mining and high complexity for some existing models. To address the above problems, we propose a graph convolutional neural network for Recommendation Based on Community Detection and the Combination of Multiple Heterogeneous graphs (GCNCMHG). This model uses the community detection algorithm to detect the communities in the user-item interaction heterogeneous graph (UIIHG), finds the regional central nodes of communities, and then creates edges between the regional central node of each community and all other nodes in the UIIHG to construct the heterogeneous partial adjacent graph. Then, a Heterogeneous Partial Adjacent Auxiliary (HPAA) layer is designed to aggregate information on the heterogeneous partial adjacent graph. HPAA layer expands the influence of distant nodes on target nodes, enables target nodes to receive global information, and enhances the ability of GCN-CMHG to mine information. Specially, due to the low complexity of HPAA layer and the abandonment of redundant information, GCNCMHG is easier to implement and train. Under the exact same experimental setting, GCN-CMHG's time consumption is only about 1/10 of another model based on GCN called graph convolutional neural network for Recommendation Based on the Combination of Multiple Heterogeneous graphs (GCNMHG). Experiments on multiple real-world datasets show that GCN-CMHG achieves better results compared with several advanced models. The implementation of our work can be found at https://***/GCNRSs/GCN-CMHG.

关键词： graph convolutional neural network community detection heterogeneous graph recommendation

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Safety analysis of new power system based on graph convolutional neural network evaluation 10

Safety analysis of new power system based on graph convoluti...

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10th International Forum on Electrical Engineering and Automation, IFEEA 2023

作者： Huang, Jianye Wu, Fei Li, Yangdi Qian, Jian Yang, Yan Yao, Wenxu State Grid Fujian Electric Power Co. Ltd. Electric Power Science Research Institute Fujian China

ISBN: (纸本)9798350309379

Although the efficient application of emerging technologies such as cloud computing, big data, and image recognition in the supervision of new power systems has effectively improved the supervisory effectiveness of power systems, it has also increased the difficulty of network security protection of power systems while improving work efficiency. In this paper, we evaluate the robustness of a graph convolutional neural network for new power systems to address the problem that artificial intelligence models for power image classification and recognition are susceptible to adversarial perturbation deception. Firstly, this paper achieves high-confidence spoofing of targets with two hops and more than two hops based on node classifiers. Firstly, this paper achieves high-confidence spoofing of targets with two hops and more than two hops based on node classifiers. Secondly, this paper establishes the POISONPROBE algorithm to improve the robustness of the attack model by searching for smaller perturbations within a longer distance from the target. Finally, experiments based on two datasets are conducted in this paper to demonstrate that the proposed POISONPROBE algorithm can be used to improve the robustness of the attack model by searching for smaller perturbations. proposed POISONPROBE algorithm has a high attack success rate. The proposed attack method in this paper can be used in future grid defense experiments to study graph convolutional neural networks with antagonistic robustness. © 2023 IEEE.

关键词： adversarial perturbation graph convolutional neural network image classification and recognition of power grid power system security

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GCN-MHSA: A novel malicious traffic detection method based on graph convolutional neural network and multi-head self-attention mechanism

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COMPUTERS & SECURITY 2024年 147卷

作者： Chen, Jinfu Xie, Haodi Cai, Saihua Song, Luo Geng, Bo Guo, Wuhao Jiangsu Univ Sch Comp Sci & Commun Engn 301 Xuefu Rd Zhenjiang 212013 Jiangsu Peoples R China Jiangsu Univ Jiangsu Key Lab Secur Technol Ind Cyberspace 301 Xuefu Rd Zhenjiang 212013 Jiangsu Peoples R China Asiainfo Secur Technol Co Ltd Nanjing 210012 Jiangsu Peoples R China

With the increasing size and complexity of network, network traffic becomes more and more correlated with each other, and the traditional manner of presenting network traffic in a Euclidean structure is difficult to effectively capture the correlation information of network traffic. In contrast, graph structured data has gained much attention in recent years due to its ability to represent the correlation between different traffic flows;In addition, models and algorithms related to graph Convolution neural network (GCN) have been used for malicious traffic detection. However, existing GCN-based malicious traffic detection methods still suffer from incomplete description of the flow-level features of network traffic, imperfect traffic correlation establishment mechanism and failure to distinguish the importance of features during model training. Based on this, this study proposes a malicious traffic detection method called GCN-MHSA based on graph convolutional neural network and Multi-Head Self-Attention mechanism. Firstly, the flow-level features of network traffic are populated and more information close to the features are selected to describe the network traffic;And then, the link homogeneity is used to establish the correlations between network traffic;Moreover, multi-head self-attention mechanism is introduced in the GCN model to provide larger weight to important features;Finally, an improved GCN is used as a deep learning model to detect malicious traffic. Extensive experimental results on three publicly available network traffic datasets and a real network traffic dataset show that the proposed GCN-MHSA method performs better than five baselines in terms of detection effect and stability, with an improvement of about 2.4% in accuracy, recall and F1-measure as well as an improvement of about 2.1% in precision.

关键词： Malicious traffic detection graph convolutional neural network Multi-head self-attention mechanism Feature extending

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Study of crystal properties based on attention mechanism and crystal graph convolutional neural network

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JOURNAL OF PHYSICS-CONDENSED MATTER 2022年第19期34卷 195901-195901页

作者： Wang, Buwei Fan, Qian Yue, Yunliang Yangzhou Univ Coll Informat Engn Yangzhou Jiangsu Peoples R China

The prediction of crystal properties has always been limited by huge computational costs. In recent years, the rise of machine learning methods has gradually made it possible to study crystal properties on a large scale. We propose an attention mechanism-based crystal graph convolutional neural network, which builds a machine learning model by inputting crystallographic information files and target properties. In our research, the attention mechanism is introduced in the crystal graph convolutional neural network (CGCNN) to learn the local chemical environment, and node normalization is added to reduce the risk of overfitting. We collect structural information and calculation data of about 36 000 crystals and examine the prediction performance of the models for the formation energy, total energy, bandgap, and Fermi energy of crystals in our research. Compared with the CGCNN, it is found that the accuracy (ACCU) of the predicted properties can be further improved to varying degrees by the introduction of the attention mechanism. Moreover, the total magnetization and bandgap can be classified under the same neural network framework. The classification ACCU of wide bandgap semiconductor crystals with a bandgap threshold of 2.3 eV reaches 93.2%, and the classification ACCU of crystals with a total magnetization threshold of 0.5 mu (B) reaches 88.8%. The work is helpful to realize large-scale prediction and classification of crystal properties, accelerating the discovery of new functional crystal materials.

关键词： attention mechanism graph convolutional neural network machine learning crystal property

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Space Target Material Identification Based on graph convolutional neural network

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REMOTE SENSING 2023年第7期15卷 1937-1937页

作者： Li, Na Gong, Chengeng Zhao, Huijie Ma, Yun Beihang Univ Inst Artificial Intelligence Beijing 100091 Peoples R China Minist Educ Key Lab Precis Optomechatron Technol Beijing 100091 Peoples R China Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Changguang Satellite Technol Co Ltd Changchun 130000 Peoples R China

Under complex illumination conditions, the spectral data distributions of a given material appear inconsistent in the hyperspectral images of the space target, making it difficult to achieve accurate material identification using only spectral features and local spatial features. Aiming at this problem, a material identification method based on an improved graph convolutional neural network is proposed. Superpixel segmentation is conducted on the hyperspectral images to build the multiscale joint topological graph of the space target global structure. Based on this, topological graphs containing the global spatial features and spectral features of each pixel are generated, and the pixel neighborhoods containing the local spatial features and spectral features are collected to form material identification datasets that include both of these. Then, the graph convolutional neural network (GCN) and the three-dimensional convolutional neural network (3-D CNN) are combined into one model using strategies of addition, element-wise multiplication, or concatenation, and the model is trained by the datasets to fuse and learn the three features. For the simulated data and the measured data, the overall accuracy of the proposed method can be kept at 85-90%, and their kappa coefficients remain around 0.8. This proves that the proposed method can improve the material identification performance under complex illumination conditions with high accuracy and strong robustness.

关键词： space target material identification complex illumination conditions graph convolutional neural network

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Cellular network Fault Diagnosis Method Based on a graph convolutional neural network

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SENSORS 2023年第16期23卷 7042-7042页

作者： Amuah, Ebenezer Ackah Wu, Mingxiao Zhu, Xiaorong Nanjing Univ Posts & Telecommun Jiangsu Key Lab Wireless Commun Nanjing 210003 Peoples R China

The efficient and accurate diagnosis of faults in cellular networks is crucial for ensuring smooth and uninterrupted communication services. In this paper, we propose an improved 4G/5G network fault diagnosis with a few effective labeled samples. Our solution is a heterogeneous wireless network fault diagnosis algorithm based on graph convolutional neural network (GCN). First, the common failure types of 4G/5G networks are analyzed, and then the graph structure is constructed with the data in the network parameter, given data sets as nodes and similarities as edges. GCN is used to extract features from the graph data, complete the classification task for nodes, and finally predict the fault types of cells. A large number of experiments are carried out based on the real data set, which is achieved by driving tests. The results show that, compared with a variety of traditional algorithms, the proposed method can effectively improve the performance of network fault diagnosis with a small number of labeled samples.

关键词： 4G/5G network graph convolutional neural network heterogeneous network fault diagnosis

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Attention enhanced capsule network for text classification by encoding syntactic dependency trees with graph convolutional neural network

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PEERJ COMPUTER SCIENCE 2022年 8卷 e831页

作者： Jia, Xudong Wang, Li Taiyuan Univ Technol Coll Data Sci Taiyuan Shanxi Peoples R China

Text classification is a fundamental task in many applications such as topic labeling, sentiment analysis, and spam detection. The text syntactic relationship and word sequence are important and useful for text classification. How to model and incorporate them to improve performance is one key challenge. Inspired by human behavior in understanding text. In this paper, we combine the syntactic relationship, sequence structure, and semantics for text representation, and propose an attention enhanced capsule network-based text classification model. Specifically, we use graph convolutional neural networks to encode syntactic dependency trees, build multi head attention to encode dependencies relationship in text sequence, merge with semantic information by capsule network at last. Extensive experiments on five datasets demonstrate that our approach can effectively improve the performance of text classification compared with state-of-the-art methods. The result also shows capsule network, graph convolutional neural network, and multi-headed attention has integration effects on text classification tasks.

关键词： Text classification Capsule network Multi-headed attention graph convolutional neural network Syntactic dependency tree

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Epilepsy EEG Seizure Prediction Based on the Combination of graph convolutional neural network Combined with Long- and Short-Term Memory Cell network

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APPLIED SCIENCES-BASEL 2024年第24期14卷 11569页

作者： Kuang, Zhejun Liu, Simin Zhao, Jian Wang, Liu Li, Yunkai Changchun Univ Sci & Technol Sch Comp Sci & Technol Changchun 130022 Peoples R China Changchun Univ Key Lab Intelligent Rehabil & Barrier Free Disable Changchun 130000 Peoples R China Changchun Univ Sch Comp Sci & Technol Jilin Prov Key Lab Human Hlth Status Identificat & Changchun 130032 Peoples R China

With the increasing research of deep learning in the EEG field, it becomes more and more important to fully extract the characteristics of EEG signals. Traditional EEG signal classification prediction neither considers the topological structure between the electrodes of the signal collection device nor the data structure of the Euclidean space to accurately reflect the interaction between signals. graph neural networks can effectively extract features of non-Euclidean spatial data. Therefore, this paper proposes a feature selection method for epilepsy EEG classification based on graph convolutional neural networks (GCNs) and long short-term memory (LSTM) cells. While enriching the input of LSTM, it also makes full use of the information hidden in the EEG signals. In the automatic detection of epileptic seizures based on neural networks, due to the strong non-stationarity and large background noise of the EEG signal, the analysis and processing of the EEG signal has always been a challenging research. Therefore, experiments were conducted using the preprocessed Boston Children's Hospital epilepsy EEG dataset, and input it into the GCN-LSTM model for deep feature extraction. The GCN network built by the graph convolution layer learns spatial features, then LSTM extracts sequence information, and the final prediction is performed by fully connected and softmax layers. The introduced method has been experimentally proven to be effective in improving the accuracy of epileptic EEG seizure detection. Experimental results show that the average accuracy of binary classification on the CHB-MIT dataset is 99.39%, and the average accuracy of ternary classification is 98.69%.

关键词： EEG signal epilepsy seizure prediction graph convolutional neural network long- and short-term memory cells GCN-LSTM

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Facial expression recognition based on landmark-guided graph convolutional neural network

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JOURNAL OF ELECTRONIC IMAGING 2022年第2期31卷 023025-023025页

作者： Meng, Hao Yuan, Fei Tian, Yang Yan, Tianhao Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China Harbin Engn Univ Minist Educ Key Lab Intelligent Technol & Applicat Marine Equ Harbin Peoples R China

convolutional neural network (CNN)-based facial emotion recognition (FER) lacks structural information, which affects the accuracy of FER. To automatically extract structural features and enrich the representation of expression features, we propose a dual-branch network based on CNN and graph CNN (GCNN) for FER. Specifically, one branch uses CNN to obtain the global features of expressions;in the parallel branch, we adopt a sparse coding strategy and propose a landmark-guided GCNN to extract the facial structure information under different expressions, enhancing the feature representation of key regions of facial expressions. The features of the two branches are fused through the channel attention mechanism to increase the semantic strength of the features to obtain more comprehensive and accurate expression features for expression classification. The entire deep neural network is trained end-to-end, and the extracted expression features are more discriminative, thereby improving the classification performance. Our experimental results prove the effectiveness of the proposed method on three publicly available datasets such as CK+ (99.24%), Fer2013 (73.26%), and Raf-db (87.42%). The proposed algorithm for extracting image structure information can be flexibly implanted into any model of image classification, image segmentation, etc., where the input image has structural information. (C) 2022 SPIE and IS&T

关键词： facial expression recognition graph convolutional neural network facial key points structural feature dual-branch

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MVGCN: Multi-View graph convolutional neural network for Surface Defect Identification Using Three-Dimensional Point Cloud

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JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME 2023年第3期145卷 031004-031004页

作者： Wang, Yinan Sun, Wenbo Jin, Jionghua (Judy) Kong, Zhenyu (James) Yue, Xiaowei Rensselaer Polytech Inst Dept Ind & Syst Engn Troy NY 12180 USA Univ Michigan Transportat Res Inst Ann Arbor MI 48109 USA Univ Michigan Dept Ind & Operat Engn Ann Arbor MI 48109 USA Virginia Tech Grad Dept Ind & Syst Engn Blacksburg VA 24060 USA

Surface defect identification is a crucial task in many manufacturing systems, including automotive, aircraft, steel rolling, and precast concrete. Although image-based surface defect identification methods have been proposed, these methods usually have two limitations: images may lose partial information, such as depths of surface defects, and their precision is vulnerable to many factors, such as the inspection angle, light, color, noise, etc. Given that a three-dimensional (3D) point cloud can precisely represent the multidimensional structure of surface defects, we aim to detect and classify surface defects using a 3D point cloud. This has two major challenges: (i) the defects are often sparsely distributed over the surface, which makes their features prone to be hidden by the normal surface and (ii) different permutations and transformations of 3D point cloud may represent the same surface, so the proposed model needs to be permutation and transformation invariant. In this paper, a two-step surface defect identification approach is developed to investigate the defects' patterns in 3D point cloud data. The proposed approach consists of an unsupervised method for defect detection and a multi-view deep learning model for defect classification, which can keep track of the features from both defective and non-defective regions. We prove that the proposed approach is invariant to different permutations and transformations. Two case studies are conducted for defect identification on the surfaces of synthetic aircraft fuselage and the real precast concrete specimen, respectively. The results show that our approach receives the best defect detection and classification accuracy compared with other benchmark methods.

关键词： 3D point cloud analytics graph convolutional neural network defect identification sensing monitoring and diagnostics

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