Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning

This repository is the official implementation of Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning.

Overview

In this project, we provide implementations of visual analysis and three models -- GCN, GCN* and MCGL-UM. The repository is organised as follows:

• data/ contains the necessary dataset files for CORA, PubMed, CiteSeer and MS Academic.
• plot/ contains the visual analysis on synthetic clean and noisy graphs.
• train_MC_base.py is the implementations of MCGL-UM.
• train_GCN.py is the implementations of GCN and GCN*.
• layers.py contains the definition of one-layer linear transformation, one-layer GCO.
• models.py contains the GCN and MLP.
• ps.py contains the command-line arguments definition with default values. By specifying arguments in this file, you can change the dataset and adjust hyper-parameters.
• utils.py contains preprocessing utilities of data loading, data split, and noise reducing,

Requirements

The project runs under Python 3.8.3 with several required dependencies:

• numpy==1.18.4
• scipy==1.4.1
• matplotlib==3.2.1
• networkx==2.4
• torchvision==0.6.0
• torch==1.5.0

In addition, CUDA 10.2 is used in this project.

Visual analysis

This paper has some visual analysis. Run commands below to get these figures.

cd plot
python comparison_clean.py
python comparison_noisy.py
python deep_GCO.py

Models

In the paper, we used three different models -- GCN, GCN* and MCGL-UM. To implement them, run respective commands below:

python train_GCN.py --baseline 1

python train_GCN.py --baseline 2

python train_MC_base.py

By specifying arguments in 'ps.py', you can change the dataset and adjust hyper-parameters. The optimal hyper-parameters we have drawn are shown below: hidden units/weight decay/learning rate/dropout rate/(batch size for MCGL-UM)

Dataset	GCN	GCN*	MCGL-UM
CORA	32/0.0005/0.005/0.7	32/0.0005/0.01/0.7	32/0.001/0.005/0.5/50
CiteSeer	64/0.001/0.05/0.6	64/0.001/0.05/0.4	64/0.001/0.005/0.3/200
PubMed	32/0.0005/0.05/0.3	32/0.0005/0.005/0.5	32/0.001/0.005/0.5/50
MS Academic	128/0.0005/0.01/0.6	128/0.0005/0.005/0.7	128/0.0001/0.005/0.5/200

By specifying arguments ("depth", "trdep", "tsdep", and "noise_rate"), you can implement different depth of GCN* and MCGL-UM, and different reduced noise rate of all three models, for example:

python train_GCN.py --baseline 2 --depth 10

python train_MC_base.py --trdep 10 --tsdep 10

python train_GCN.py --baseline 1 --noise_rate 0.1

Datasets

In this paper, we use three citation datasets and a co-authorship dataset, which can be downloaded in https://github.com/tkipf/gcn/tree/master/gcn/data and https://github.com/klicperajo/ppnp/tree/master/ppnp/data. CORA, CiteSeer and PubMed are citation graphs, where a node represents a paper, and an edge between two nodes represents that the two papers have a citation relationship. MS Academic is co-author graph, an edge in the graph represents the co-authorship between two papers.