This repository contains PyTorch implementation of the following paper: "Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation"
enviroment setup: "run conda install -f enviroment.yml"
installation of Graph automorphism library: https://web.cs.dal.ca/~peter/software/pynauty/html/install.html
To replicate the results in the paper for MNIST and CIFAR10 dataset, run the following commands:
#DGMG
sh experiments/DGMG_caveman_small.sh
sh experiments/DGMG_ENZYMES.sh
#GraphRNN
sh experiments/GraphRNN_caveman_small.sh
sh experiments/GraphRNN_Lung.sh
#Graphgen
sh experiments/Graphgen_citeseer_small.sh
sh experiments/Graphgen_ENZYMES.sh
To list the arguments, run the following command:
python main.py -h
To train the given model on Lung dataset, run the following:
python main.py \
--graph_tyep Lung \
--note <GraphRNN, DGMG, Graphgen> \
--sample_size 16 \
--gcn_type <gat, gcn, appnp> \
--max_cr_iteration 5 \
----enable_gcn \
To train the given model on ENZYMES dataset, run the following:
python main.py \
--graph_tyep ENZYMES \
--note <GraphRNN, DGMG, Graphgen> \
--sample_size 16 \
--gcn_type <gat, gcn, appnp> \
--max_cr_iteration 5 \
----enable_gcn \
To train the given model on caveman_small dataset, run the following:
python main.py \
--graph_tyep caveman_small \
--note <GraphRNN, DGMG, Graphgen> \
--sample_size 16 \
--gcn_type <gat, gcn, appnp> \
--max_cr_iteration 5 \
----enable_gcn \
To train the given model on citeseer_small dataset, run the following:
python main.py \
--graph_tyep citeseer_small \
--note <GraphRNN, DGMG, Graphgen> \
--sample_size 16 \
--gcn_type <gat, gcn, appnp> \
--max_cr_iteration 5 \
----enable_gcn