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e-lstm-d

This is a TensorFlow implementation of the paper: E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction. The baselines used in the paper will be released as a toolbox soon.

Requirements

  • tensorflow (1.3.0)
  • keras (2.2.4)
  • scikit-learn (0.19.0)
  • numpy (1.14.2)

run the demo

The framework of E-LSTM-D

We provide the framework of E-LSTM-D and the detailed structure of it when applied on LKML.

Data processing

  1. prepare the data
mkdir data
tar -xzvf contact.tar.gz ./data
  1. train model
python train_model.py --dataset contact --encoder [128] --lstm [256,256] --decoder [274] --num_epochs 1600 --batch_size 32 --BETA 10 --learning_rate 0.001

Cite

Please cite our paper if you use this code in your own work:

@article{chen2019lstm,
  title={E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction},
  author={Chen, Jinyin and Zhang, Jian and Xu, Xuanheng and Fu, Chengbo and Zhang, Dan and Zhang, Qingpeng and Xuan, Qi},
  journal={arXiv preprint arXiv:1902.08329},
  year={2019}
}

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