The description of "Element-guided Temporal Graph Representation Learning for Temporal Sets Prediction" at WWW 2022 is available here.

The original data could be downloaded from here. You can download the data and then put the data files in the ./original_data folder.

1. run ./preprocess_data/preprocess_data_{dataset_name}.py to preprocess the original data, where dataset_name could be DC, TaoBao, JingDong and TMS. We also provide the preprocessed datasets at here, which should be put in the ./dataset folder.

2. run ./train/train_ETGNN.py to train the model on different datasets using the configuration in ./utils/config.json.

3. run ./evaluate/evaluate_ETGNN.py to evaluate the model. Please make sure the config in evaluate_ETGNN.py keeps identical to that in the model training process.

• PyTorch 1.8.1
• tqdm
• numpy
• pandas

Hyperparameters can be found in ./utils/config.json file, and you can adjust them when training the model on different datasets.

Please consider citing our paper when using the codes or datasets.

@inproceedings{DBLP:conf/www/YuWS0L22,
  author    = {Le Yu and
               Guanghui Wu and
               Leilei Sun and
               Bowen Du and
               Weifeng Lv},
  title     = {Element-guided Temporal Graph Representation Learning for Temporal
               Sets Prediction},
  booktitle = {{WWW} '22: The {ACM} Web Conference 2022, Virtual Event, Lyon, France,
               April 25 - 29, 2022},
  pages     = {1902--1913},
  publisher = {{ACM}},
  year      = {2022}
}