[1] 沈学利, 关刘美, 翟宇琦. 基于卷积神经网络的颜色修正水下图像增强方法[J]. 计算机技术与发展, 2024, 34(8): 42-48.
SHEN X L, GUAN L M, ZHAI Y Q. Color correction underwater image enhancement method based on convolutional neural network[J]. Computer Technology and Development, 2024, 34(8): 42-48.
[2] 李莉, 彭娜, 王巍. 基于轻量级卷积神经网络的遥感图像检测模型[J]. 计算机工程与设计, 2023, 44(5): 1511-1518.
LI L, PENG N, WANG W. Remote sensing image detection model based on lightweight convolutional neural network[J]. Computer Engineering and Design, 2023, 44(5): 1511-1518.
[3] 谭琬滢, 左珊珊, 邱佩琳, 等. 基于深度卷积神经网络的手写数字识别研究[J]. 智能计算机与应用, 2024, 14(8): 138-142.
TAN W Y, ZUO S S, QIU P L, et al. Research on handwritten digit recognition based on deep convolutional neural network[J]. Intelligent Computers and Applications, 2024, 14(8): 138-142.
[4] 朱克佳. 基于深度学习的目标检测研究[J]. 现代信息科技, 2024, 8(13): 76-83.
ZHU K J. Research on object detection based on deep learning[J]. Modern Information Technology, 2024, 8(13): 76-83.
[5] BRONSTEIN M M, BRUNA J, LECUN Y, et al. Geometric deep learning: going beyond euclidean data[J]. IEEE Signal Processing Magazine, 2017, 34(4): 18-42.
[6] MASCI J, BOSCAINI D, BRONSTEIN M, et al. Geodesic convolutional neural networks on riemannian manifolds[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops. Santiago, Chile: IEEE, 2015: 37-45.
[7] BOSCAINI D, MASCI J, RODOLÀ E, et al. Learning shape correspondence with anisotropic convolutional neural networks[C]//Advances in Neural Information Processing Systems. Barcelona, Spain: MIT Press, 2016: 29.
[8] ATWOOD J, TOWSLEY D. Diffusion-convolutional neural networks[C]//Advances in Neural Information Processing Systems. Barcelona, Spain: MIT Press, 2016: 29.
[9] 李文静, 白静, 彭斌, 等. 图卷积神经网络及其在图像识别领域的应用综述[J]. 计算机工程与应用, 2023, 59(22): 15-35.
LI W J, BAI J, PENG B, et al. A review of graph convolutional neural networks and their applications in image recognition[J]. Computer Engineering and Applications, 2023, 59(22): 15-35.
[10] WU F, SOUZA A, ZHANG T, et al. Simplifying graph convolutional networks[C]//International Conference on Machine Learning. Long Beach, United States: ACM, 2019: 6861-6871.
[11] 王启明, 胥津铭, 苏建, 等. 基于改进ALexNet模型的路面状况识别方法研究[J]. 公路交通科技, 2023, 40(3): 209-218.
WANG Q M, XU J M, SU J, et al. Research on road condition recognition method based on improved ALexNet model[J]. Highway Traffic Technology, 2023, 40(3): 209-218.
[12] SAHOO P K, PANDA M K, PANIGRAHI U, et al. Animproved VGG-19 network induced enhanced feature pooling for precise moving object detection in complex video scenes[J]. IEEE Access, 2024, 12: 45847-45864.
[13] HASSAN E, HOSSAIN M S, SABER A, et al. A quantum convolutional network and ResNet (50)-based classification architecture for the MNIST medical dataset[J]. Biomedical Signal Processing and Control, 2024, 87: 105560.
[14] 张艺博, 赵加坤, 陈攀, 等. 基于改进Mask RCNN的遥感图像小目标检测算法研究[J]. 计算机与数字工程, 2024, 52(3): 880-885.
ZHANG Y B, ZHAO J K, CHEN P, et al. Research on small target detection algorithm in remote sensing images based on improved Mask RCNN[J]. Computer and Digital Engineering, 2024, 52(3): 880-885.
[15] 孙顺远, 杨镇. 基于改进Faster RCNN的目标检测算法[J]. 计算机与数字工程, 2022, 50(12): 2654-2659.
SUN S Y, YANG Z. Object detection algorithm based on improved Faster RCNN[J]. Computer and Digital Engineering, 2022, 50(12): 2654-2659.
[16] 米增, 连哲. 面向通用目标检测的YOLO方法研究综述[J]. 计算机工程与应用, 2024, 60(21): 38-54.
MI Z, LIAN Z. A review of YOLO methods for general object detection[J]. Computer Engineering and Applications, 2024, 60(21): 38-54.
[17] BRESSON X, LAURENT T, VON BRECHT J. Enhanced Lasso recovery on graph[C]//2015 23rd European Signal Processing Conference. Nice, France: IEEE, 2015: 1501-1505.
[18] SHAHID N, PERRAUDIN N, KALOFOLIAS V, et al. Fast robust PCA on graphs[J]. IEEE Journal of Selected Topics in Signal Processing, 2016, 10(4): 740-756.
[19] 孙伟, 陈平华. 基于知识图谱上下文的图注意矩阵补全[J]. 计算机工程与应用, 2022, 58(11): 171-177.
SUN W, CHEN P H. Graph attention matrix completion based on knowledge graph context[J]. Computer Engineering and Applications, 2022, 58(11): 171-177.
[20] SCARSELLI F, GORI M, TSOI A C, et al. The graph neural network model[J]. IEEE Transactions on Neural Networks, 2008, 20(1): 61-80.
[21] 张其, 陈旭, 王叔洋, 等. 动态图神经网络链接预测综述[J]. 计算机工程与应用, 2024, 60(20): 49-67.
ZHANG Q, CHEN X, WANG S Y, et al. A review of dynamic graph neural network link prediction[J]. Computer Engineering and Applications, 2024, 60(20): 49-67.
[22] 肖国庆, 李雪琪, 陈玥丹, 等. 大规模图神经网络研究综述[J]. 计算机学报, 2024, 47(1): 148-171.
XIAO G Q, LI X Q, CHEN Y D, et al. A review of research on large-scale graph neural networks[J]. Journal of Computer Science, 2024, 47(1): 148-171.
[23] DEFFERRARD M, BRESSON X, VANDERGHEYNST P. Convolutional neural networks on graphs with fast localized spectral filtering[C]//Advances in Neural Information Processing Systems. Barcelona, Spain: MIT Press, 2016: 29.
[24] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[C]//International Conference on Learning Representations. Toulon, France: OpenReview, 2017.
[25] GROVER A, LESKOVEC J. node2vec: Scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. San Francisco, USA: ACM, 2016: 855-864.
[26] VELIČKOVIĆ P, CUCURULL G, CASANOVA A, et al. Graph attention networks[C]//International Conference on Learning Representations. Vancouver, Canada: OpenReview, 2018.
[27] 侯磊, 刘金环, 于旭, 等. 图神经网络研究综述[J]. 计算机科学, 2024, 51(6): 282-298.
HOU L, LIU J H, YU X, et al. A review of research on graph neural networks[J]. Computer Science, 2024, 51(6): 282-298.
[28] CAO W, YAN Z, HE Z, et al. A comprehensive survey on geometric deep learning[J]. IEEE Access, 2020, 8: 35929-35949.
[29] 刘金利, 张培玲. 改进LeNet-5网络在图像分类中的应用[J]. 计算机工程与应用, 2019, 55(15): 32-37, 95.
LIU J L, ZHANG P L. Application of improved LeNet-5 network in image classification[J]. Computer Engineering and Applications, 2019, 55(15): 32-37, 95.
[30] YANG Z, COHEN W, SALAKHUDINOV R. Revisiting semi-supervised learning with graph embeddings[C]//International Conference on Machine Learning. New York, USA: ACM, 2016: 40-48.
[31] YANG K, WANG X, ZHENG Y. Atext categorization method using graph convolutional network based on sparse representation[C]//2019 Chinese Control Conference. Guangzhou, China: IEEE, 2019: 8756-8759.
[32] JOHNSON R, ZHANG T. Semi-supervised convolutional neural networks for text categorization via region embedding[C]//Advances in Neural Information Processing Systems. Montreal, Canada: MIT Press, 2015, 28.
[33] ISCEN A, TOLIAS G, AVRITHIS Y, et al. Label propagation for deep semi-supervised learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE, 2019: 5070-5079.
[34] BERAHMAND K, NASIRI E, ROSTAMI M, et al. A modified DeepWalk method for link prediction in attributed social network[J]. Computing, 2021, 103: 2227-2249.
[35] CONG G, LIM S H. Versatile feature learning with graph convolutions and graph structures[C]//2021 International Conference on Data Mining Workshops. Auckland, New Zealand: IEEE, 2021: 669-677.
[36] XU J, LI K, LI Z, et al.Fuzzy graph convolutional network for hyperspectral image classification[J]. Engineering Applications of Artificial Intelligence, 2024, 127: 107280.
