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Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks (TKDD 2019)

License: MIT License

Python 13.55% Jupyter Notebook 86.45%
social-network-analysis link-prediction recurrent-neural-networks autoencoder machine-learning nonparametric-model heterogeneous-information-networks dynamic-networks

npglm's Introduction

Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks

This repository is the official implementation of the paper Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks, published in ACM Transactions on Knowledge Discovery from Data (TKDD), Auguest, 2019.

Paper:

Available on arXiv: https://arxiv.org/pdf/1710.00818

Datasets

1. DBLP

Download Link: https://static.aminer.org/lab-datasets/citation/dblp.v8.tgz
extract to data/dblp

2. Delicious

Download Link: http://files.grouplens.org/datasets/hetrec2011/hetrec2011-delicious-2k.zip
extract to data/delicious

3. MovieLens

Download Link: http://files.grouplens.org/datasets/hetrec2011/hetrec2011-movielens-2k-v2.zip
extract to data/movielens

Usage

Check out main.ipynb notebook.

Citation

If you find this code useful, please cite the following paper:

@article{10.1145/3333028,
    author = {Sajadmanesh, Sina and Bazargani, Sogol and Zhang, Jiawei and Rabiee, Hamid R.},
    title = {Continuous-Time Relationship Prediction in Dynamic Heterogeneous Information Networks},
    year = {2019},
    issue_date = {August 2019},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    volume = {13},
    number = {4},
    issn = {1556-4681},
    journal = {ACM Trans. Knowl. Discov. Data},
    month = jul,
    articleno = {44},
}

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