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

Backward flow of gradients in RNN can explode or vanish. Exploding is controlled with gradient clipping. Vanishing is controlled with additive interactions (LSTM) Better understanding (both …

GraphRNN learns to generate graphs by training on a represen-tative set of graphs and decomposes the graph gen-eration process into a sequence of node and edge formations, …

In this lecture, we present the Graph Recurrent Neural Networks. We define GRNN as particular cases of RNN in which the signals at each point in time are supported on a graph. In this …

Jesse Bettencourt and Harris Chan have made a great slide introducing GraphRNN in Prof. David Duvenaud’s seminar course Learning Discrete Latent Structure.

This repository is the official PyTorch implementation of GraphRNN, a graph generative model using auto-regressive model. Jiaxuan You *, Rex Ying *, Xiang Ren , William L. Hamilton , …

Dec 12, 2024 · GraphRNN is able to generate more realistic and structured graphs compared to random graph generation and other generative models, which makes it particularly useful in …

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, …

Our experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also …

Our experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also …