Instant Graph Neural Networks for Dynamic Graphs

Requirements

• CUDA 10.1
• python 3.8.5
• pytorch 1.7.1
• GCC 5.4.0
• cython 0.29.21
• eigency 1.77
• numpy 1.18.1
• torch-geometric 1.6.3
• tqdm 4.56.0
• ogb 1.2.4
• [eigen 3.3.9] (https://gitlab.com/libeigen/eigen.git)

Datasets

OGB Datasets can be downloaded from here. The website 'Open Graph Benchmark' provides an automatic method to download and convert the three datasets. So you can straightly run 'python convert_ogb.py' instead of downloading these datasets manually. We drop several edges to simulate the graphs' evolving nature. In the folder './convert/', we provide the codes to convert the three datasets.

We generate a real dataset with dynamic labels, Aminer, which is processed from the raw data. The processed version can be downloaded from here.

In our paper, we also use synthetic datasets generated by the SBM. In the folder './convert/', we provide the codes to generate and convert the datasets. For example, you can run the following codes to generate SBM-500K

    g++ -std=c++11 gen_SBM.cpp -o rd_dynamic
    ./rd_dynamic -n 500000 -c 50 -ind 20 -outd 1 -snap 10 -change 2500

Compilation

Cython needs to be compiled before running, run this command:

python setup.py build_ext --inplace

Running the code

• On OGB datasets

./ogb.sh

• On the Aminer dataset

./aminer.sh

• On SBM datasets

./sbm.sh