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graph convolutional network With Self-Supervised Learning for Brain Disease Classification

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024年第6期21卷 1830-1841页

作者： Wang, Guangyu Chu, Ying Wang, Qianqian Zhang, Limei Qiao, Lishan Liu, Mingxia Shandong Jianzhu Univ Sch Comp Sci & Technol Jinan 250101 Peoples R China Ulster Univ Fac Comp Engn & Built Environm Coleraine BT52 1SA County Londonde Ireland Donghua Univ Sch Informat Sci & Technol Shanghai 201620 Peoples R China Univ North Carolina Chapel Hill Dept Radiol Chapel Hill NC 27599 USA Univ North Carolina Chapel Hill Biomed Res Imaging Ctr Chapel Hill NC 27599 USA

Brain functional network (BFN) analysis has become a popular method for identifying neurological diseases at their early stages and revealing sensitive biomarkers related to these diseases. Due to the fact that BFN is a graph with complex structure, graph convolutional networks (GCNs) can be naturally used in the identification of BFN, and can generally achieve an encouraging performance if given large amounts of training data. In practice, however, it is very difficult to obtain sufficient brain functional data, especially from subjects with brain disorders. As a result, GCNs usually fail to learn a reliable feature representation from limited BFNs, leading to overfitting issues. In this paper, we propose an improved GCN method to classify brain diseases by introducing a self-supervised learning (SSL) module for assisting the graph feature representation. We conduct experiments to classify subjects with mild cognitive impairment (MCI) and autism spectrum disorder (ASD) respectively from normal controls (NCs). Experimental results on two benchmark databases demonstrate that our proposed scheme tends to obtain higher classification accuracy than the baseline methods.

关键词： Diseases Task analysis Self-supervised learning Feature extraction Reliability Vectors Pipelines Brain functional network graph convolutional network self-supervised learning brain disease classification

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graph convolutional network with STC attention and adaptive normalization for skeleton-based action recognition

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IMAGING SCIENCE JOURNAL 2023年第7期71卷 636-646页

作者： Zhou, Haiyun Xiang, Xuezhi Qiu, Yujian Liu, Xuzhao Nanjing Forest Police Coll Coll Publ Secur Nanjing Peoples R China Harbin Engn Univ Sch Informat & Commun Engn Harbin 150001 Peoples R China Minist Ind & Informat Technol Key Lab Adv Marine Commun & Informat Technol Harbin 150001 Peoples R China

graph convolutional network (GCN) have been widely used in the field of skeleton-based action recognition and have achieved exciting results. Introducing attention mechanism in the process of extracting skeleton features has always been a hot spot in GCN-related research. In this article, we design a new graph convolutional network, which combines the advanced decoupling graph convolutional network (DC-GCN) with spatial, temporal, channel (STC) series attention module and adaptive normalization (AN). The STC attention module helps the network tend to extract important information from skeleton features. In addition, in order to improve the adaptability of the normalization method to GCN, we design the AN module instead of the BN module, which can train the weights of different normalization methods, so that each normalization layer in the network adopts the most suitable normalization operation. The experimental results show that the accuracy of our method is competitive with the state-of-the-art action recognition methods.

关键词： Skeleton-based action recognition Skeleton features graph convolutional network STC attention module Spatial attention Temporal attention Channel attention adaptive normalization

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graph convolutional network Based Multi-Objective Meta-Deep Q-Learning for Eco-Routing

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2024年第7期25卷 7323-7338页

作者： Ma, Xin Xie, Yuanchang Chigan, Chunxiao Univ Massachusetts Lowell Dept Elect & Comp Engn Lowell MA 01854 USA Univ Massachusetts Dept Civil & Environm Engn Lowell MA 01854 USA

Route selection can greatly affect vehicle fuel consumption and emissions. Finding the most fuel/energy-efficient route is known as the eco-routing problem. Existing eco-routing solutions do not effectively consider the critical traffic signal information and rely on fuel consumption models that may not be sufficiently accurate. To address the eco-routing problem in a signalized traffic network, this paper proposes a graph convolutional network based multi-objective meta-deep Q-learning (GM(2)DQL)method. The problem is formulated as dynamic multi-objective Markov decision processes (MOMDP) and is tackled through deep reinforcement learning and meta-learning. We identify that graph convolutional network (GCN) is an efficient and suitable feature representation for a signalized traffic network. GM2DQLcan explore the optimal routes with respect to drivers' different preferences on saving fuel and travel time. Through GM2DQL,the agent is trained under a series of learning environments that are characterized by historical vehicle trajectories, fuel consumption data, and traffic signal data in the remote datacenter. The vehicle requesting eco-routing service can download the model that represents the action value function of the historical dynamic driving conditions. The model in the vehicle can quickly adapt to the most recent driving condition through online one-shot learning and predict the optimal eco-routes for the subsequent unseen driving conditions of the signalized traffic network. Extensive proof-of-concept experiments validate thatGM2DQL can effectively discover optimal eco-routes. It saves up to 71% travel time and 62% fuel, compared to the conventional shortest-path routing strategy that is widely used in navigation systems

关键词： Eco-routing signalized traffic network meta-learning graph convolutional network deep Q-learning

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graph convolutional network With Unknown Class Number

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IEEE TRANSACTIONS ON MULTIMEDIA 2023年 25卷 4800-4813页

作者： Ye, Xulun Zhao, Jieyu Ningbo Univ Inst Comp Sci & Technol Ningbo 315211 Zhejiang Peoples R China

The graph convolutional network (GCN), as a powerful tool in graph data processing, is widely exploited in many machine learning and computer vision tasks. However, existing GCNs usually assume that the network has fixed outputs, which is usually contrary to the real-world class number being unknown and incremental, leading to an open set classification problem in which the finite training dataset cannot contain all labels in the infinite testing data. To overcome these issues, a novel Bayesian model is proposed, in which we couple GCN and a deep generative clustering model in a unified framework. In our model, the GCN model is used to detect the known classes, the deep generative clustering model is designed to generate the novel classes, and a two-level label generative process is constructed to extend the finite GCN outputs to infinity and fuse the label generated by the GCN model and the deep generative model. Although posterior inference is difficult, our model leads to an efficient variational inference-based optimization method. Experiments on various datasets validate our theoretical analysis and demonstrate that our model can achieve state-of-the-art performance. Our source code has been released on the website.

关键词： Clustering dirichlet process graph convolutional network open set learning variational inference

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graph convolutional network-based security-constrained unit commitment leveraging power grid topology in learning

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ENERGY REPORTS 2023年 9卷 3544-3552页

作者： Tang, Xian Bai, Xiaoqing Weng, Zonglong Wang, Rui Guangxi Univ Sch Elect Engn Guangxi Key Lab Power Syst Optimizat & Energy Savi Nanning 530004 Peoples R China

Security-constrained unit commitment (SCUC) is a complex optimization problem in power system operation, which is computationally intensive. To bring significant time-savings, this paper presents a graph convolutional network (GCN)-based SCUC approach (GCN-SCUC) using the information of power grid topology. Instead of tackling the mixed integer linear programming (MILP)-based SCUC (MILP-SCUC), the GCN learner predicts the unit decisions first, and then the MILP-SCUC problem is transformed into a continuous convex one. Numerical experiments are performed on the modified IEEE-30 and IEEE-118 systems to verify the feasibility of our approach both in terms of accuracy and computation time. Moreover, compared with the state-of-the-art MILP-SCUC, the proposed approach achieves speedups of between 13x and 17x on different testing examples with high accuracy.(c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Security -constrained unit commitment graph convolutional network GCN-SCUC

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graph convolutional network-based reinforcement learning for tasks offloading in multi-access edge computing

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第19期80卷 29163-29175页

作者： Leng, Lixiong Li, Jingchen Shi, Haobin Zhu, Yi'an Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China

To achieve high quality of service for computation-intensive applications, multi-access edge computing (MEC) is proposed for offloading tasks to MEC servers. The emerging reinforcement learning-based task offloading strategies have attracted attention of researchers, but the incomplete Markov models in them result in limited improvements. This work proposes a graph convolutional network-based reinforcement learning (GRL-based) method to enhance the reinforcement learning-based task offloading in MEC. The graph convolutional network is introduced to extract features from tasks through regarding the task set as a directed acyclic graph. Then we construct a complete Markov model for the offloading strategy. In the proposed GRL-based method, the decision process is deployed in the user layer, while the training process is deployed in the cloud layer. An off-policy reinforcement learning method, soft actor-critic, is used to train the offloading strategy, by which the sampling and training can be implemented separately. Several simulation experiments show the proposed GRL-based method performs better than baseline methods, and it can achieve continuous decisions for task offloading efficiently.

关键词： Multi-access edge computing graph convolutional network Reinforcement learning

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graph convolutional network Aided Virtual network Embedding for Internet of Thing

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IEEE TRANSACTIONS ON network SCIENCE AND ENGINEERING 2023年第1期10卷 265-274页

作者： Ma, Sihan Yao, Haipeng Mai, Tianle Yang, Jingkai He, Wenji Xue, Kaipeng Guizani, Mohsen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Univ Sci & Technol China Dept Elect Engn Hefei 230052 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi 44737 U Arab Emirates

The past few years have seen the dramatic adoption of the Internet of Things (IoT) in everyday life, from manufacturing to healthcare. With the emergence of various new Internet of Things applications, it is a challenging problem to meet the different QoS requirements of Internet of Things applications in shared substrate networks. Recently, network Virtualization (NV) has attracted a large amount of attention from academia and industry. NV enables multiple virtual networks to coexist on the same substrate network, thus providing IoT users with customized end-to-end services. The main challenge of NV is the Virtual network Embedding (VNE) problem, which refers to embed different virtual networks into one substrate network. Inspired by the recent success of graph convolutional network (GCN) in graph structured data processing, in this paper, we propose a GCN aided VNE algorithm. The GCN can extract high-order spatial structure information among substrate nodes through the convolution kernel. Considering that the training data of VNE has no label, we introduce the policy gradient algorithm to optimize the GCN model. In addition, three evaluation metrics are designed to evaluate the performance of the network embedding policy. Some simulations are implemented to evaluate our proposed algorithm in comparison to the other state-of-the-art solutions.

关键词： Substrates Internet of Things Measurement Neural networks Kernel Feature extraction Heuristic algorithms graph convolutional network policy gradient reinforcement learning virtual network embedding

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graph convolutional network with triplet attention learning for person re-identification

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INFORMATION SCIENCES 2022年 617卷 331-345页

作者： Saber, Shimaa Amin, Khalid Plawiak, Pawel Tadeusiewicz, Ryszard Hammad, Mohamed Menoufia Univ Fac Comp & Informat Dept Informat Technol Menoufia Egypt Cracow Univ Technol Fac Comp Sci & Telecommun Dept Comp Sci Warszawska 24 PL-31155 Krakow Poland Polish Acad Sci Inst Theoret & Appl Informat Baltycka 5 PL-44100 Gliwice Poland AGH Univ Sci & Technol Dept Biocybernet & Biomed Engn Krakow Poland

Person re-identification (re-ID) is a method that uses several non-overlapping cameras to identify the same individual. Person re-ID has been employed successfully in a diversity of computer vision applications. This task is made more difficult by occlusions, abrupt illumi-nation, pose changes among camera views, cluttered backgrounds, and inaccurate detec-tions. Therefore, we propose a new graph convolutional network with attention modules. This research reveals a new attention network that encompasses the encoder -decoder and the triplet attention module. The proposed attention module employs the self-attention process to achieve potent and discriminatory features by utilizing temporal, spatial, and channel context information. The triplet attention module is utilized to capture cross-dimension dependencies and pedestrian features, and also reduces the impact of the imperfect pedestrian image to remedy the occlusion issue. The encoder-decoder is used to observe the whole-body shape. Experiments on several publicly available datasets reveal that our method has a high degree of generalization and outperforms existing methods. On Market1501, the proposed method outperformed the recent approaches with an accu-racy of 92.98% for rank-1. According to the results, our method ameliorates quantitative and qualitative person re-ID methods.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： graph convolutional network Triplet attention module Person re-identification Encoder-decoder attention module

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graph convolutional network-Based Repository Recommendation System

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 175-196页

作者： Zhifang Liao Shuyuan Cao Bin Li Shengzong Liu Yan Zhang Song Yu School of Computer Science and Engineering Central South UniversityChangshaChina School of Information Technology and Management Hunan University of Finance and EconomicsChangshaChina School of Engineering and Built Environment Glasgow Caledonian UniversityGlasgowUK

GitHub repository recommendation is a research hotspot in the field of open-source software. The current problemswith the repository recommendation systemare the insufficient utilization of open-source community informationand the fact that the scoring metrics used to calculate the matching degree between developers and repositoriesare developed manually and rely too much on human experience, leading to poor recommendation results. Toaddress these problems, we design a questionnaire to investigate which repository information developers focus onand propose a graph convolutional network-based repository recommendation system (GCNRec). First, to solveinsufficient information utilization in open-source communities, we construct a Developer-Repository networkusing four types of behavioral data that best reflect developers’ programming preferences and extract features ofdevelopers and repositories from the repository content that developers focus on. Then, we design a repositoryrecommendation model based on a multi-layer graph convolutional network to avoid the manual formulation ofscoringmetrics. Thismodel takes the Developer-Repository network, developer features and repository features asinputs, and recommends the top-k repositories that developers are most likely to be interested in by learning theirpreferences. We have verified the proposed GCNRec on the dataset, and by comparing it with other open-sourcerepository recommendation methods, GCNRec achieves higher precision and hit rate.

关键词： Repository recommendation graph convolutional network open-source software GitHub

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graph convolutional network-Based Model for Megacity Real Estate Valuation

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IEEE ACCESS 2022年 10卷 104811-104828页

作者： Yang, Zongyan Hong, Zhonghua Zhou, Ruyan AI, Hong Shanghai Ocean Univ Coll Informat Technol Shanghai 201306 Peoples R China

It is challenging to make precise assessments of real estate prices due to its elevated individual prices, complicated influencing factors, and ambiguous attribute selection. As a result of the high demand for owner-occupied and investment properties, real estate is also a substantial concern for society. A hot topic for research by major institutions has been how to accurately estimate its price. Real-world applications of real estate valuation impose stringent requirements on the acquisition of datasets and the generalizability of models. On the basis of SRGCNN, a spatial regression model with excellent generalizability, this paper introduces an external attention mechanism to construct the A-SRGCNN model and compares it to the benchmark model utilizing data from Shanghai, Melbourne, and San Diego. For spatial regression, A-SRGCNN employs graph convolutional neural networks, and the external attention mechanism implicitly considers the relationship between property data. Experiments indicate that the A-SRGCNN model outperforms the benchmark model and has improved real estate price estimation accuracy. In the meantime, this paper employs the A-SRGCNN model to conduct zonal experiments and time-division experiments on the secondary real estate market in Shanghai to analyze the real estate price linkages between different zones and the real estate price linkages at different times. It is revealed that Shanghai real estate prices exhibit spatial aggregation and price aggregation, with comparable prices within the same zones, and that the A-SRGCNN model is effective at predicting house prices.

关键词： Biological system modeling Data models Random forests Predictive models Prediction algorithms convolutional neural networks Cost accounting graph convolutional network deep learning real estate valuation spatial analysis

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