GraphRNN 可以视作一种级联形式，由两个RNN组成： edge-level RNN：为新生成的节点生成新的边。 Key Idea： 将不同节点顺序下的图表示为序列，并在这些序列上构建一个自回归的生 …

2018年2月24日 · Our experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, …

GraphRNN models a graph in an autoregressive (or recurrent) manner—as a sequence of additions of new nodes and edges—to capture the complex joint probability of all nodes and …

GraphRNN以自回归（或递归）的方式对图进行建模，来捕捉图中所有节点和边的复杂联合概率。特别的是，GraphRNN可以被看做是一个分层模型，其中一个图级的RNN保持图的状态并生成 …

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models - JiaxuanYou/graph-generation

2021年11月18日 · 在序列的演进方向进行 递归 （recursion）且所有节点（循环单元）按链式连接的 递归神经网络 （recursive neural network）。 基础的神经网络包含输入层、隐层、输出 …

2024年12月12日 · GraphRNN is a deep learning model that specialises in graph generation and is particularly good at learning the structure of a graph and generating new graphs. The model …

这是CS224W Machine Learning with Graph学习笔记第10篇 - GraphRNN。 本文主要介绍GraphRNN的基本原理。Tips：由于图生成模型的学习难度略大，小李同学白天做项目，晚上 …

In this lecture, we explore an application of GRNNs – an epidemic modeling – and compare them with GNNs and RNNs. To model the epidemic we will use a real-world dataset of a highschool …

GraphRNN learns to generate graphs by training on a representative set of graphs and decomposes the graph generation process into a sequence of node and edge formations, …