application in image processing, this paper presents the mainstream structure model for image classification based on the graph neural network, and synthetically compares the performance of the more popular graph neural network model by analyzing the application scenarios and solving problems of the model...
综述论文“Graph Neural Networks: A Review of Methods“ 我上礼拜在CSDN写的,直接转过来。 arXiv于2019年7月10日上载的GNN综述论文“Graph Neural Networks: A Review of Methods and Applications“。 CSDN-专业IT技术社区-登录摘录两段,关于GN… 黄浴发表于深度学习在... 阅读文献笔记:Graph neural networks:...
论文题目:A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions 期刊信息:ACM, 2023 作者机构: - Beijing National Research Center for Information Science and Technology (BNRist), - Department of Electronic Engineering, - Tsinghua University, - School of Informati...
论文题目:A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions 期刊信息:ACM, 2023 作者机构: Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, School of Information Science a...
递归图神经网络 (recurrent graph neural networks, RecGNNs) 卷积图神经网络 (convolutional graph neural networks, ConvGNN) 图自编码器 (graph autoencoders, GAE) 考虑时间因素的图神经网络 (spatial-temporal graph neural networks, ST-GNN) 讨论图神经网络在各个领域的应用 总结了 GNN 的开源代码、基准数据...
图生成网络 Graph Generative Networks 分子生成对抗网络 Molecular Generative Adversarial Networks (MolGAN) Deep Generative Models of Graphs (DGMG) 图时空网络Graph Spatial-Temporal Networks 扩散卷积递归神经网络 Diffusion Convolutional Recurrent Neural Network (DCRNN) ...
香港大学2022年发表的一篇关于GNN和Graph Transformer在视觉领域的综述。 摘要 图神经网络(GNNs)在图表示学习中获得了动力,并在许多领域推动了最新技术的发展,例如数据挖掘(如社交网络分析和推荐系统)、计算机视觉(如物体检测和点云学习)以及自然语言处理(如关系提取和序列学习)等等。随着自然语言处理和计算机视觉中Transfo...
Learning Steady-States of Iterative Algorithms over Graphs:递归图神经网络代表 时空图神经网络# 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 ...
Graph neural networks (GNNs) aim to learn well-trained representations in a lower-dimension space for downstream tasks while preserving the topological structures. In recent years, attention mechanism, which is brilliant in the fields of natural language processing and computer vision, is introduced to...
(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:聚合节点 ...