cNRL: Contrastive Network Representation Learning

About

• Python3 implemetation of cNRL from Fujiwara et al., 2022.
  • Fujiwara et al., Network Comparison with Interpretable Contrastive Network Representation Learning, Journal of Data Science, Statistics, and Visualisation, forthcoming (arXiv:2005.12419).

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Requirements

• Python3

• OS: macOS or Linux

  • Note: cNRL itself is available from Windows but DeepGL used in the above paper is not available from Windows.

• To run sample.py, also ccPCA and DeepGL.

  • ccPCA: https://github.com/takanori-fujiwara/ccpca
  • DeepGL: https://github.com/takanori-fujiwara/deepgl

• Note: Tested on macOS Ventura and Ubuntu 20.0.4 LTS.

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Setup

• Install with pip3. Move to the directory of this repository. Then,

  pip3 install .

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Usage

• Import an installed module from python (e.g., from cnrl import CNRL). See sample.py for examples.

• For detailed documentations, please see doc/index.html or directly see comments in cnrl.py.

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How to Cite

Please cite: Fujiwara et al., Network Comparison with Interpretable Contrastive Network Representation Learning. Journal of Data Science, Statistics, and Visualisation, forthcoming (arXiv:2005.12419).

Also, there is one more closely related work: Fujiwara et al., A Visual Analytics Framework for Contrastive Network Analysis. In Proc. IEEE Conference on Visual Analytics Science and Technology (VAST), 2020.