CRIPAC-DIG / DGCF

[ICDM 2020] Python implementation for "Dynamic Graph Collaborative Filtering."

DGCF

This is the code for the ICDM 2020 Paper: Dynamic Graph Collaborative Filtering.

Usage

Our code mainly mainly refers to jodie: https://github.com/srijankr/jodie/, data download method, running environment and initialization are the same as jodie.

Train model

To train the DGCF model using the data/<network>.csv dataset, use the following command. This will save a model for every epoch in the saved_models/<network>/ directory.

python DGCF.py --network <network> --model DGCF --epochs 50 --method attention --adj

This code can be given the following command-line arguments:

--network: choose to the train data:reddit\wikipedia\lastfm

--model: this is the name of the model

--epochs: this is the maximum number of interactions to train the model.

--embedding_dim: this is the number of dimensions of the dynamic embedding.

--method: this is the type of aggregator function in second-order aggregation

--adj: this is a boolean input indicating if use the second update.

--length： this is the aggregator size in second-order aggegator function.

Evaluate the model

To evaluate the performance of the interaction prediction task in one epoch, use the following command:

python evaluate_interaction_prediction.py --network --model --method

To evaluate the performance of the interaction prediction task in all epoch, user the followinig command:

python evaluate_all.py --network --model --method

For detailed code execution, you can refer to the command line in the ./Shell folder.

Requirements

• python3
• jsonlines
• pytorch
• torch-geometric

Citation

Please cite our paper if you use the code:

@inproceedings{li2020dynamic,
  title={Dynamic graph collaborative filtering},
  author={Li, Xiaohan and Zhang, Mengqi and Wu, Shu and Liu, Zheng and Wang, Liang and Philip, S Yu},
  booktitle={2020 IEEE International Conference on Data Mining (ICDM)},
  pages={322--331},
  year={2020},
  organization={IEEE}
}