A Survey of Geometric Graph Neural Networks Data Structures, Models and Applications.docx 1.5M· 百度网盘 A Survey of Geometric Graph Neural Networks Data Structures, Models and Applications.pdf 2.3M· 百度网盘 摘要 几何图是一种具有几何特征的特殊图,对于建模许多科学问题至关重要。与通用图不同,几何...
[LG] A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications http://t.cn/A6YnfkUy 深入探讨了几何图神经网络(Geometric GNN),这种网络专门处理具有几何特征的图数据结构...
当然,貌似他们课题组的也对某些相关方面进行研究,有兴趣的可以参考综述《Graph Neural Networks: A Review of Methods and Applications》、《A comprehensive survey on graph neural networks》,将来我也会对这些文献进行总结分享。 文中还提到,Network embedding也是一个很火的研究点,可以实现将节点嵌入到低维的向量...
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition:人体动作识别 Structural-RNN: Deep Learning on Spatio-Temporal Graphs.:驾驶员行为预测 Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting:交通流量预测 图神经网络主要的框架# 节点级# ...
GraphESN 提高了GNN*的训练效率 Gated Graph Neural Network (GGNN) 采用门控递归单元(GRU)作为递归函数,将递归减少到固定的步数。其优点是,它不再需要约束参数来确保收敛。 隐藏状态更新函数: GGNN采用bp -propagation through time (BPTT)算法来学习模型参数。对于大型图来说,这可能是一个问题,因为GGNN需要在所有...
instead of computing similarity scores based on predefined measures. We emphasize that this paper does not attempt to survey the extensive literature on graph representation learning, graph neural networks, and graph embedding. Prior work has focused on these topics (see Cai et al.2018; Goyal and...
Due to the unique construction module and design of graph neural networks (GNNs), neural architecture search (NAS) methods specifically for GNNs have becom... Y Liu,J Liu - 《Applied Soft Computing》 被引量: 0发表: 2023年 A Survey on Neural Architecture Search Based on Reinforcement Learning...
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Xiao Luo, Philip S. Yu, Ming Zhang ...
JOURNALOFL A T E XCLASSFILES,VOL.X,NO.X,DECEMBER20181 AComprehensiveSurveyonGraphNeural Networks ZonghanWu,ShiruiPan,Member,IEEE,FengwenChen,GuodongLong, ChengqiZhang,SeniorMember,IEEE,PhilipS.Yu,Fellow,IEEE Abstract—Deeplearninghasrevolutionizedmanymachinelearningtasksinrecentyears,rangingfromimageclassi...
A. Taxonomy of Graph Neural Networks (1)Recurrent Graph Neural Networks: GNN的先驱,其目的是学习具有循环神经结构的节点表示,RecGNN假设图中的一个节点不断地与它的邻居交换信息/消息,直到达到稳定的均衡。 (2)Convolutional Graph Neural Networks: ConvGNN将网格数据的卷积运算推广到Graph数据。主要思想:聚合节...