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CSC-GCN:Contrastive semantic calibration for graph convolution network

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Journal of Information and Intelligence

Journal of Information and Intelligence 2023年第4期1卷 295-307页

作者： Xu Yang Kun Wei Cheng Deng School of Electronic Engineering Xidian UniversityXi'an 710071China

graph convolutional networks(GCNs)have been successfully applied to node representation learning in various real-world applications.However,the performance of GCNs drops rapidly when the labeled data are severely scarce,and the node features are prone to being indistinguishable with stacking more layers,causing over-fitting and over-smoothing problems.In this paper,we propose a simple yet effective contrastive semantic calibration for graph convolution network(CSC-GCN),which integrates stochastic identity aggregation and semantic calibration to overcome these weaknesses.The basic idea is the node features obtained from different aggregation operations should be similar.Toward that end,identity aggregation is utilized to extract semantic features from labeled nodes,while stochastic label noise is adopted to alleviate the over-fitting problem.Then,contrastive learning is employed to improve the discriminative ability of the node features,and the features from different aggregation operations are calibrated according to the class center similarity.In this way,the similarity between unlabeled features and labeled ones from the same class is enhanced while effectively reducing the over-smoothing problem.Experimental results on eight popular datasets show that the proposed CSC-GCN outperforms state-ofthe-art methods on various classification tasks.

关键词： graph convolution network Contrastive learning Semi-supervised learning

Self-attention Based Multimodule Fusion graph convolution network for Traffic Flow Prediction

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国际计算机前沿大会会议论文集 2022年第1期 3-16页

作者： Lijie Li Hongyang Shao Junhao Chen Ye Wang College of Computer Science and Technology Harbin Engineering UniversityHarbinChina

With rapid economic development,the per capita ownership of automobiles in our country has begun to rise year by year.More researchers have paid attention to using scientific methods to solve traffic flow problems.Traffic flow prediction is not simply affected by the number of vehicles,but also contains various complex factors,such as time,road conditions,and people flow.However,the existing methods ignore the complexity of road conditions and the correlation between individual nodes,which leads to the poor performance.In this study,a deep learning model SAMGCN is proposed to effectively capture the correlation between individual nodes to improve the performance of traffic flow prediction.First,the theory of spatiotemporal decoupling is used to divide each time of each node into finer particles.Second,multimodule fusion is used to mine the potential periodic relationships in the data.Finally,GRU is used to obtain the potential time relationship of the three modules.Extensive experiments were conducted on two traffic flow datasets,PeMS04 and PeMS08 in the Caltrans Performance Measurement System to prove the validity of the proposed model.

关键词： Flow prediction Temporal-spatial correlation graph convolution network Self-attention mechanism

Building Indoor Dangerous Behavior Recognition Based on LSTM-GCN with Attention Mechanism

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Computer Modeling in Engineering & Sciences 2023年第11期137卷 1773-1788页

作者： Qingyue Zhao Qiaoyu Gu Zhijun Gao Shipian Shao Xinyuan Zhang School of Management Shenyang Jianzhu UniversityShenyang110168China School of Information and Communication Engineering North University of ChinaTaiyuan030051China School of Information and Control Engineering Shenyang Jianzhu UniversityShenyang110168China

Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition.A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism(GLA)model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features.The network connects GCN and LSTMnetwork in series,and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction,which fully excavates the temporal and spatial features of the skeleton sequence.Finally,an attention layer is designed to enhance the features of key bone points,and Softmax is used to classify and identify dangerous behaviors.The dangerous behavior datasets are derived from NTU-RGB+D and Kinetics data sets.Experimental results show that the proposed method can effectively identify some dangerous behaviors in the building,and its accuracy is higher than those of other similar methods.

关键词： Human skeleton building indoor dangerous behaviors recognition graph convolution network long short term memory network attention mechanism

Deep 3D mesh watermarking with self-adaptive robustness

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Cybersecurity 2023年第2期6卷 75-88页

作者： Feng Wang Hang Zhou Han Fang Weiming Zhang Nenghai Yu University of Science and Technology of China HefeiChina Simon Fraser University VancouverCanada National University of Singapore Kent RidgeSingapore

Robust 3D mesh watermarking is a traditional research topic in computer graphics,which provides an efficient solution to the copyright protection for 3D meshes.Traditionally,researchers need manually design watermarking algorithms to achieve suffcient robustness for the actual application scenarios.In this paper,we propose the first deep learning-based 3D mesh watermarking network,which can provide a more general framework for this problem.In detail,we propose an end-to-end network,consisting of a watermark embedding sub-network,a watermark extracting sub-network and attack layers.We employ the topology-agnostic graph convolutional network(GCN)as the basic convolution operation,therefore our network is not limited by registered meshes(which share a fixed topology).For the specific application scenario,we can integrate the corresponding attack layers to guarantee adaptive robustness against possible attacks.To ensure the visual quality of watermarked 3D meshes,we design the curvature consistency loss function to constrain the local geometry smoothness of watermarked meshes.Experimental results show that the proposed method can achieve more universal robustness while guaranteeing comparable visual quality.

关键词： 3D mesh watermarking graph convolution network Attack layer

graph convolutional network Based Fault Detection and Identification for Low-voltage DC Microgrid

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Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 917-926页

作者： Ambuj Pandey Soumya R.Mohanty Department of Electrical Engineering Indian Institute of Technology(BHU)VaranasiUttar PradeshIndia IEEE

This paper presents a novel fault detection and identification method for low-voltage direct current(DC)microgrid with meshed configuration.The proposed method is based on graph convolutional network(GCN),which utilizes the explicit spatial information and measurement data of the network topology to identify a fault.It has a more substantial feature extraction ability even in the presence of noise and bad data.The adjacency matrix for GCN is developed by considering the network topology as an inherent graph.The bus voltage and line current samples after faults are regarded as the node attributes.Moreover,the DC microgrid model is developed using PSCAD/EMTDC simulation,and fault simulation is carried out by considering different possible events that include environmental and physical conditions.The performance of the proposed method under different conditions is compared with those of different machine learning techniques such as convolutional neural network(CNN),support vector machine(SVM),and fully connected network(FCN).The results reveal that the proposed method is more effective than others at detecting and classifying faults.This method also possesses better robustness under the presence of noise and bad data.

关键词： DC microgrid graph convolution network fault detection topological information

ST-Trader:A Spatial-Temporal Deep Neural network for Modeling Stock Market Movement

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IEEE/CAA Journal of Automatica Sinica 2021年第5期8卷 1015-1024页

作者： Xiurui Hou Kai Wang Cheng Zhong Zhi Wei the Department of Computer Science New Jersey Institute of TechnologyNewarkNJ 07102 USA the School of Business Stevens Institute of TechnologyHobokenNJ 07030 USA

Stocks that are fundamentally connected with each other tend to move together.Considering such common trends is believed to benefit stock movement forecasting tasks.However,such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile data.Motivated by this observation,we propose a framework that incorporates the inter-connection of firms to forecast stock prices.To effectively utilize a large set of fundamental features,we further design a novel pipeline.First,we use variational autoencoder(VAE)to reduce the dimension of stock fundamental information and then cluster stocks into a graph structure(fundamentally clustering).Second,a hybrid model of graph convolutional network and long-short term memory network(GCN-LSTM)with an adjacency graph matrix(learnt from VAE)is proposed for graph-structured stock market forecasting.Experiments on minute-level U.S.stock market data demonstrate that our model effectively captures both spatial and temporal signals and achieves superior improvement over baseline methods.The proposed model is promising for other applications in which there is a possible but hidden spatial dependency to improve time-series prediction.

关键词： graph convolution network long-short term memory network stock market forecasting variational autoencoder(VAE)

Semi-GSGCN: Social Robot Detection Research with graph Neural network

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Computers, Materials & Continua 2020年第10期65卷 617-638页

作者： Xiujuan Wang Qianqian Zheng Kangfeng Zheng Yi Sui Jiayue Zhang Information Technology Institute Beijing University of TechnologyBeijing100124China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing100876China

Malicious social robots are the disseminators of malicious information on social networks,which seriously affect information security and network environments.Efficient and reliable classification of social robots is crucial for detecting information manipulation in social networks.Supervised classification based on manual feature extraction has been widely used in social robot detection.However,these methods not only involve the privacy of users but also ignore hidden feature information,especially the graph feature,and the label utilization rate of semi-supervised algorithms is low.Aiming at the problems of shallow feature extraction and low label utilization rate in existing social network robot detection methods,in this paper a robot detection scheme based on weighted network topology is proposed,which introduces an improved network representation learning algorithm to extract the local structure features of the network,and combined with the graph convolution network(GCN)algorithm based on the graph filter,to obtain the global structure features of the network.An end-to-end semi-supervised combination model(Semi-GSGCN)is established to detect malicious social robots.Experiments on a social network dataset(cresci-rtbust-2019)show that the proposed method has high versatility and effectiveness in detecting social robots.In addition,this method has a stronger insight into robots in social networks than other methods.

关键词： Social networks social robot detection network representation learning graph convolution network

Weighted Forwarding in graph convolution networks for Recommendation Information Systems

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Computers, Materials & Continua 2024年第2期78卷 1897-1914页

作者： Sang-min Lee Namgi Kim Department of Computer Science Kyonggi UniversitySuwonKorea

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.graph convolution network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised graph Learning(SGL)and Simple graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

关键词： Deep learning graph neural network graph convolution network graph convolution network model learning method recommender information systems

Distributed Photovoltaic Real-Time Output Estimation Based on graph convolutional networks

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Journal of Shanghai Jiaotong university(Science) 2024年第2期29卷 290-296页

作者：陈利跃洪道鉴何星卢东祁张乾谢妮娜徐一洲应煌浩 State Grid Zhejiang Electric Power Co. Ltd.Hangzhou 310007China State Grid Zhejiang Taizhou Power Supply Company Taizhou 318000ZhejiangChina Department of Automation School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China

The rapid growth of distributed photovoltaic(PV)has remarkable influence for the safe and economic operation of power systems.In view of the wide geographical distribution and a large number of distributed PV power stations,the current situation is that it is dificult to access the current dispatch data network.According to the temporal and spatial characteristics of distributed PV,a graph convolution algorithm based on adaptive learning of adjacency matrix is proposed to estimate the real-time output of distributed PV in regional power grid.The actual case study shows that the adaptive graph convolution model gives different adjacency matrixes for different PV stations,which makes the corresponding output estimation algorithm have higher accuracy.

关键词： distributed photovoltaic(PV) graph convolution network power estimation