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Python3 implemetation of DeepGL.
- This package is implemented for: Fujiwara et al., Network Comparison with Interpretable Contrastive Network Representation Learning, JDSSV, 2022.
- Related repository: Contrastive Network Representation Learning (cNRL), https://github.com/takanori-fujiwara/cnrl
- Original DeepGL paper: Rossi et al., Deep Inductive Graph Representation Learning, IEEE TKDE, 2018.
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Current implementation supports a major portion of DeepGL. However, for example, local graphlet count-based features are not supported. These functionality will be tentatively implemented in the future.
- Python3
- graph-tool (https://graph-tool.skewed.de/)
- OS: macOS or Linux
- Note: Tested on macOS Sonoma and Ubuntu 20.0.4 LTS.
- Windows is not supported because graph-tool is not available for Windows.
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Install graph-tool (https://graph-tool.skewed.de/installation.html)
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For example, macOS with Homebrew (when not using virtual environment),
brew install graph-tool -
When using virtual environment, there are two options:
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Option 1. Follow the graph-tool instruction (need a lot of time for compiling graph-tool).
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Check a section of "Installing in a virtualenv".
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graph-tool's instruction doesn't support Python3.12. For Python3.12, before the configure step (i.e., ./configure --prefix=$HOME/.local), run commands below:
pip3 install setuptools pycairo
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Option 2. Use virtual environment with "include-system-site-packages = true"
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Either make venv with --system-site-packages option (e.g.,
python3 -m venv --system-site-packages venv) or edit "pyenv.cfg" (include-system-site-packages = true) -
then
brew install graph-tool
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Install with pip3. Move to the directory of this repository. Then,
pip3 install .
- Import installed modules from python (e.g.,
from deepgl import DeepGL). See sample.py for examples. - For detailed documentations, please see doc/index.html or directly see comments in deepgl.py.
- If you use this implementation of DeepGL, please consider to cite: Fujiwara et al., Network Comparison with Interpretable Contrastive Network Representation Learning, JDSSV, 2022.