Skip to content
master
Switch branches/tags
Code
This branch is 7 commits behind Networks-Learning:master.
Contribute

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Designing Random Graph Models Using Variational Autoencoders With Applications to Chemical Design

Paper : https://arxiv.org/pdf/1802.05283.pdf

Required packages

-tensorflow 1.4.1

-rdkit >= 2016.03.4

-networkx 2.0

Command to learn model

python main.py --num_epochs 10 --learning_rate 0.0001 --log_every 5 --z_dim <z> --random_walk <k> --edges <e> --nodes <n> --graph_file <graph> --z_dir <zspace> --sample_file <sampledir> --out_dir <outputdir> > log.out

Command to sample graph

python sample.py --num_epochs 10 --learning_rate 0.0001 --log_every 5 --z_dim <z> --random_walk <k> --edges <e> --nodes <n> --graph_file <graph> --z_dir <zspace> --sample_file <sample> --out_dir <outputfile> > log.out

For real data please checkout the branch node_label

About

Code and data for "NeVAE: A Deep Generative Model for Molecular Graphs", AAAI 2019

Resources

Releases

No releases published

Packages

No packages published

Languages