N-Gram Graph

This repository contains the source code for the paper

Shengchao Liu, Mehmet Furkan Demirel, Yingyu Liang. N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules. NeurIPS 2019 (Spotlight).

You can check the paper on NeurIPS proceedings or ArXiv.

1. Env Setup

Install Anaconda2-4.3.1 first, and below is an example on Linux.

wget https://repo.continuum.io/archive/Anaconda2-4.3.1-Linux-x86_64.sh
bash Anaconda2-4.3.1-Linux-x86_64.sh -b -p ./anaconda
export PATH=$PWD/anaconda/bin:$PATH

Then set up the env.

conda env create -f gpu_env.yml
source activate n_gram_project
pip install --user -e .

2. Data Preparation

cd datasets
bash download_data.sh
bash data_preprocess.sh

Below is the specification of all the datasets & tasks.

Dataset	# of Tasks	Task Type
Delaney	1	Regression
Malaria	1	Regression
CEP	1	Regression
QM7	1	Regression
QM8	12	Regression
QM9	12	Regression
Tox21	12	Classification
Clintox	2	Classification
MUV	17	Classification
HIV	1	Classification

3. Run Models

There are two test.sh scripts (under path n_gram_graph/ and n_gram_graph/embedding/) for quick test on task Delaney.

3.1 Run the Node-Level and Graph-Level Embedding

• First specify the arguments.

cd n_gram_graph/embedding

export task=...
export running_index=...

• Run the node-level embedding:

mkdir -p ./model_weight/"$task"/"$running_index"

python node_embedding.py \
--mode="$task" \
--running_index="$running_index"

• Run the graph-level embedding:

mkdir -p ../../datasets/"$task"/"$running_index"

python graph_embedding.py \
--mode="$task" \
--running_index="$running_index"

Please check run_embedding.sh for detailed specifications.

3.2 Run RF and XGB

• First specify arguments.

cd n_gram_graph

export task=...
export model=...
export weight_file=...
export running_index=...

• For classification tasks:

python main_classification.py \
--task="$task" \
--config_json_file=../config/"$model"/"$task".json \
--weight_file="$weight_file" \
--running_index="$running_index" \
--model="$model" 

• For regression tasks:

python main_regression.py \
--task="$task" \
--config_json_file=../config/"$model"/"$task".json \
--weight_file="$weight_file" \
--running_index="$running_index" \
--model="$model" 

Please check run_n_gram_classification.sh and run_n_gram_regression.sh for detailed specifications.

4. Citation

@incollection{NIPS2019_9054,
    title = {N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules},
    author = {Liu, Shengchao and Demirel, Mehmet F and Liang, Yingyu},
    booktitle = {Advances in Neural Information Processing Systems 32},
    editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
    pages = {8464--8476},
    year = {2019},
    publisher = {Curran Associates, Inc.},
    url = {http://papers.nips.cc/paper/9054-n-gram-graph-simple-unsupervised-representation-for-graphs-with-applications-to-molecules.pdf}
}