DMM: Dynamic Multi-Network Mining of Tensor Time Series

Implementation of DMM, Kohei Obata, Koki Kawabata, Yasuko Matsubara, Yasushi Sakurai. The Web Conference 2024, WWW'24.

Requirements

Install Python 3.8, and the required dependencies.
Required dependencies can be installed by: pip install -r requirements.txt

pip install numpy
pip install pandas
pip install matplotlib
pip install sklearn

Data Preparation

Synthetic datasets

cd data
python Synthetic.py

Air-quality dataset

Download the Beijing Multi-Site Air-Quality Data Data Set from UCI. Move them into the data folder. (/DMM/data/PRSA_Data_20130301-20170228)

Google dataset

(/DMM/data/google/commerce)

Usage

Synthetic experiments

python experiment_synthetic.py

Realdata experiments

python experiment_realdata.py

Citation

If you use this code for your research, please consider citing our WWW paper.

@inproceedings{10.1145/3589334.3645461,
author = {Obata, Kohei and Kawabata, Koki and Matsubara, Yasuko and Sakurai, Yasushi},
title = {Dynamic Multi-Network Mining of Tensor Time Series},
year = {2024},
isbn = {9798400701719},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3589334.3645461},
doi = {10.1145/3589334.3645461},
booktitle = {Proceedings of the ACM on Web Conference 2024},
pages = {4117–4127},
numpages = {11},
keywords = {clustering, graphical lasso, network inference, tensor time series},
location = {, Singapore, Singapore, },
series = {WWW '24}
}

KoheiObata/DMM