图神经网络（Graph Neural Networks，GNN）综述 - 知乎
在本文中，我们将图神经网络划分为五大类别，分别是： 图卷积网络 （Graph Convolution Networks，GCN）、 图注意力网络 （Graph Attention Networks）、 图自编码器 （ Graph Autoencoders）、 图生成网络 （ Graph Generative Networks） 和 图时空网络 （Graph Spatial-temporal Networks）。
Graph neural network - Wikipedia
Graph neural networks (GNN) are specialized artificial neural networks that are designed for tasks whose inputs are graphs. [1] [2] [3] [4] [5] One prominent example is molecular drug design. [6] [7] [8] Each input sample is a graph representation of a molecule, where atoms form the nodes and chemical bonds between atoms form the edges. In ...
【图神经网络综述】一文道尽GNN原理、框架和应用-CSDN博客
图神经网络（gnn）作为一种专门用于处理图结构数据的深度学习模型，在多个领域取得了显著成果。随着研究的深入和技术的不断发展，gnn将在更多领域得到应用和推广。
A Gentle Introduction to Graph Neural Networks - Distill
2021年9月2日 · Now that the graph’s description is in a matrix format that is permutation invariant, we will describe using graph neural networks (GNNs) to solve graph prediction tasks. A GNN is an optimizable transformation on all attributes of the graph (nodes, edges, global-context) that preserves graph symmetries (permutation invariances).
A Comprehensive Introduction to Graph Neural Networks (GNNs)
2022年7月21日 · Graph Neural Networks are special types of neural networks capable of working with a graph data structure. They are highly influenced by Convolutional Neural Networks (CNNs) and graph embedding. GNNs are used in predicting nodes, edges, and graph-based tasks. CNNs are used for image classification.
GNN 系列（一）：Graph 基础知识介绍 - 知乎 - 知乎专栏
图卷积神经网络 (Graph Convolutional Network)作为最近几年兴起的一种基于图结构的广义神经网络结构，因为其独特的计算能力，而受到广泛学者的关注与研究。
圖神經網路（Graph Neural Networks, GNNs）完整入門指南
2 天之前 · 這些網路在數學上表現為「圖」（graphs），也是人工智慧最令人振奮領域之一：圖神經網路（GNNs）的基礎。 ... 每個路段都被視為節點，具有當前車速、時間與天氣等特徵。GNN 能學習道路網路中不同區域之間的複雜依存關係。 ...
图神经网络GNN综述：《Graph Neural Networks: A Review of …
2023年3月30日 · 本文翻译自图神经网络综述：《Graph Neural Networks: A Review of Methods and Applications》全文共3.5万字，该论文系统地回顾了图神经网络（GNNs）的方法和应用，包括 图卷积网络 （GCN）、GraphSAGE、 图注意力网络 （GAT）等，为图神经网络领域的研究者和实践者提供了一个 ...
Graph neural networks: A review of methods and applications
2020年1月1日 · Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks.
Graph Neural Networks: An In-Depth Introduction and Practical ...
2024年5月30日 · Graph Neural Networks (GNNs) are a class of artificial neural networks designed to process data that can be represented as graphs. Unlike traditional neural networks that operate on Euclidean data (like images or text), GNNs are tailored to handle non-Euclidean data structures, making them highly versatile for various applications.