6 datasets were used in this paper:
- Wikipedia: downloadable from http://snap.stanford.edu/jodie/.
- Reddit: downloadable from http://snap.stanford.edu/jodie/.
- MOOC: downloadable from http://snap.stanford.edu/jodie/.
- AskUbuntu: downloadable from http://snap.stanford.edu/data/sx-askubuntu.html.
- SuperUser: downloadable from http://snap.stanford.edu/data/sx-superuser.html.
- Wiki-Talk: downloadable from http://snap.stanford.edu/data/wiki-talk-temporal.html.
If edge features or nodes features are absent, they will be replaced by a vector of zeros. Example usage:
python utils/preprocess_data.py --data wikipedia --bipartite
python uitls/preprocess_custom_data.py --data superuser
- PyTorch 1.7.1
- Python 3.8
- Numba 0.54.1
Optional arguments:
--data Dataset name
--bs Batch size
--n_degree Number of neighbors to sample
--n_head Number of heads used in attention layer
--n_epoch Number of epochs
--n_layer Number of network layers
--lr Learning rate
--gpu GPU id
--patience Patience for early stopping
--enable_random Use random seeds
--gradient Disable gradient blocking
--reuse Enable caching and reuse
--budget Cache size
Example usage:
python train.py --n_epoch 50 --n_layer 2 --bs 200 -d wikipedia --enable_random --reuse --lr 1e-4 --gpu 1
python train.py --n_epoch 50 --n_layer 2 --bs 200 -d askubuntu --enable_random --reuse --budget 1000 --lr 1e-7 --gpu 1