N-Gram Graph

Authors: Shengchao Liu, Mehmet Furkan Demirel, Yingyu Liang.

This is 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).

Prevailing methods for small-molecule property prediction are not quite stable, especially when we focus on each individual task for each datasets.

Here we propose another research line doing N-Gram representation on molecular graphs. The motivation is that, instead of the K-hop neighborhood, the N-Gram path/walk provides a finer-grained view with more flexibility. We also provide a solid analysis on the representation power of N-Gram Graph.

Another thing to point out is that the graph-level representation is indeed learning in an unsupervised (self-supervised) way. The N-Gram Graph pipeline can be divided into three main parts:

Vertex embedding, unsupervised (self-supervised)
Graph embedding, unsupervised
A simple classifier

For the empirical results, N-Gram Graph with XGBoost is in top-1 for 21 out of 60 tasks, and is in top-3 for 48. Recall that our setting is for each individual task, not for each dataset. You can check the full 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. Cite Us

@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}
}

chao1224/n_gram_graph