Regularized Simple Graph Convolution for link prediction

by Patrick Pho (Phuong Pho) and Alexander V. Mantzaris

This repo is an official implementation of the Regularized Simple Graph Convolution (SGC) for link prediction task in our paper - "Link prediction with Simple Graph Convolution and regularized Simple Graph Convolution".

We adopt the flexible regularization scheme introduced in our previous work - "Regularized Simple Graph Convolution (SGC) for improved interpretability of large datasets" - for link predictor module's weight vector. The $L_1$ term reduces the number of components of the weight vectors, the $L_2$ term controls the overall size of the weight vectors. The proposed framework produces sparser set of fitted weights highlighting important edge embeddings that define link likelihood.

Prerequisites

The dependencies can be install via:

pip install -r requirement.txt

For GPU machine, please refer to official instruction to install suitable version of pytorch and dgl:

• PyTorch
• Deep Graph Library - DGL

Data

Three citation datasets (Cora, Citeseer, and Pubmed) are available for user to experiment with our framework. These datasets are included in DGL package and can be selected by specifying --dataset argument (see example in the Usage section).

We also provide utility function import_data to assist users in importing their own dataset.

Usage

Train model

An example of incorporating $L_1 = 0.5, L_2 = 1.0,$ into SGC fitted on Cora dataset is:

python main.py --dataset cora --L1 0.5 --L2 1

Use --save-trained to save trained model for inference. The trained model is save in ./checkpoints

python main.py --dataset cora --L1 0.5 --L2 1 --save-trained

Other useful options for training:

• --early-stop: turn on early stopping to reduce overfitting. Default metric is loss
• --hist-print: print training history at every t epoch