Contrastive Self-supervised Learning for Graph Classification

This is a PyTorch implementation of the methods proposed in the paper.

Requirements

• python3.7
• pytorch==1.4.0
• CUDA==10.1
• torch-scatter==2.0.4
• torch-sparse==0.6.1
• torch-cluster==1.5.4
• torch-geometric==1.4.2

Datasets

Graph classification benchmarks are publicly available at here.

This folder contains the following comma separated text files (replace DS by the name of the dataset):

n = total number of nodes

m = total number of edges

N = number of graphs

(1) DS_A.txt (m lines)

sparse (block diagonal) adjacency matrix for all graphs, each line corresponds to (row, col) resp. (node_id, node_id)

(2) DS_graph_indicator.txt (n lines)

column vector of graph identifiers for all nodes of all graphs, the value in the i-th line is the graph_id of the node with node_id i

(3) DS_graph_labels.txt (N lines)

class labels for all graphs in the dataset, the value in the i-th line is the class label of the graph with graph_id i

(4) DS_node_labels.txt (n lines)

column vector of node labels, the value in the i-th line corresponds to the node with node_id i

There are OPTIONAL files if the respective information is available:

(5) DS_edge_labels.txt (m lines; same size as DS_A_sparse.txt)

labels for the edges in DS_A_sparse.txt

(6) DS_edge_attributes.txt (m lines; same size as DS_A.txt)

attributes for the edges in DS_A.txt

(7) DS_node_attributes.txt (n lines)

matrix of node attributes, the comma seperated values in the i-th line is the attribute vector of the node with node_id i

(8) DS_graph_attributes.txt (N lines)

regression values for all graphs in the dataset, the value in the i-th line is the attribute of the graph with graph_id i

Run

CSSL-Pretrain

In this approach, we first pretrain the graph encoder with CSSL and then finetune it in a supervised way.

For pretraining, we can execute the following command:

python main_moco.py --gpu 0 --cos --dataset NCI1 --lr 1e-5 --epochs 1000 -b 16

After pretraining, the model will be saved in the folder './results/'+args.dataset+'/'+str(args.batch_size)'. We can then load the model and finetune it. For example, we can execute the following command:

python finetune.py --dataset NCI1 --device cuda:0 --resume ./results/NCI1/16/checkpoint_00001.pth.tar --batch_size 16

CSSL-Freeze

The pretraining process is shown above. After pre-training, we fix the graph encoder and train an MLP to conduct classification. For example, we can execute the following command:

python cls.py --dataset NCI1 --device cuda:0 --resume ./results/NCI1/16/checkpoint_00001.pth.tar --batch_size 16

CSSL-Reg

In this method, we view CSSL task as a regularizer.

If we train and test on a single dataset, execute:

python reg.py --dataset NCI1 --gpu 0 -b 16

If we train on all datasets and test on a specific dataset, execute:

python reg_all.py --dataset all --test_dataset NCI1 --gpu 0 -b 16