jayadratha / tgn

A PyTorch annotated replication of the paper: https://arxiv.org/abs/2006.10637

TGN

A PyTorch annotated replication of twitter-research/tgn

Paper: Temporal Graph Networks for Deep Learning on Dynamic Graphs

Requirements

Python >= 3.6

pandas==1.1.0
torch==1.6.0
scikit_learn==0.23.1

Preprocess datasets

Download the public data

Download the sample datasets (eg. wikipedia and reddit) from here and store their csv files in a folder named ./data

Preprocess the data

We use the dense npy format to save the features in binary format. If edge features or nodes features are absent, they will be replaced by a vector of zeros.

python utils/preprocess_data.py --data wikipedia
python utils/preprocess_data.py --data reddit

Model training

Self-supervised learning using the link prediction task:

# TGN-attn: self-supervised learning on the wikipedia dataset
python link_prediction.py --data wikipedia --embedding_module graph_sum --use_memory --memory_update_at_start

# TGN-attn-reddit: self-supervised learning on the reddit dataset
python link_prediction.py --data reddit --embedding_module graph_sum --use_memory --memory_update_at_start

** Check more commands with --help

TODOs

• Add code for training on the downstream node-classification task (semi-supervised setting)

Cite the paper

@inproceedings{tgn_icml_grl2020,
    title={Temporal Graph Networks for Deep Learning on Dynamic Graphs},
    author={Emanuele Rossi and Ben Chamberlain and Fabrizio Frasca and Davide Eynard and Federico 
    Monti and Michael Bronstein},
    booktitle={ICML 2020 Workshop on Graph Representation Learning},
    year={2020}
}