iLoRE

Codes for "iLoRE: Dynamic Graph Representation Learning with Instant Long-term Modeling and Re-occurrence Preservation" paper

Data

Please download data from the here and pre-process them with the script provided by TGN.

How to use

For the temporal link prediction task, for example, please run:

python train_self_supervised.py --data [DATA] --bs [batch_size] --m_bs [mini_batch_size] --count_dim [xx] --block_number [2,3...]

For the node classification task, for example, please run:

python train_supervised.py --data [DATA] --bs [batch_size] --m_bs [mini_batch_size] --count_dim [xx] --block_number [2,3...]

You should run the temporal link prediction task first.

Cite

@inproceedings{zhang2023iLoRE,
  title={iLoRE: Dynamic Graph Representation Learning with Instant Long-term Modeling and Re-occurrence Preservation},
  author={Siwei, Zhang and Yun, Xiong and Yao, Zhang and Xixi, Wu and Yiheng, Sun and Jiawei, Zhang},
  booktitle={The 32nd ACM International Conference on Information and Knowledge Management},
  year={2023}
}