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GitHub / medea-learner / Spectral-Clustering-with-Graph-Neural-Networks-for-Graph-Pooling
Experimental results obtained with the MinCutPool layer as presented in the 2020 ICML paper "Spectral Clustering with Graph Neural Networks for Graph Pooling"
Fork of FilippoMB/Spectral-Clustering-with-Graph-Neural-Networks-for-Graph-Pooling
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Forks: 0
Open Issues: 0
License: mit
Language:
Repo Size: 2.94 MB
Dependencies:
49
Created: over 3 years ago
Updated: 9 months ago
Last pushed: over 3 years ago
Last synced: 9 months ago
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Dependencies
requirements.txt
pypi
- Keras ==2.2.4
- Keras-Applications ==1.0.8
- Keras-Preprocessing ==1.1.2
- Markdown ==3.2.2
- Pillow ==7.1.2
- PyGSP ==0.5.1
- PyWavelets ==1.1.1
- PyYAML ==5.3.1
- Werkzeug ==1.0.1
- absl-py ==0.9.0
- astor ==0.8.1
- certifi ==2020.4.5.1
- chardet ==3.0.4
- cycler ==0.10.0
- decorator ==4.4.2
- gast ==0.2.2
- google-pasta ==0.2.0
- grpcio ==1.29.0
- h5py ==2.10.0
- idna ==2.9
- imageio ==2.8.0
- importlib-metadata ==1.6.0
- joblib ==0.14.1
- kiwisolver ==1.1.0
- lxml ==4.5.1
- matplotlib ==3.0.3
- networkx ==2.3
- numpy ==1.18.4
- opt-einsum ==3.2.1
- pandas ==0.24.2
- protobuf ==3.12.2
- pygraphviz ==1.5
- pyparsing ==2.4.7
- python-dateutil ==2.8.1
- pytz ==2020.1
- requests ==2.23.0
- scikit-image ==0.15.0
- scikit-learn ==0.22.2.post1
- scipy ==1.4.1
- six ==1.15.0
- spektral ==0.1.2
- tensorboard ==1.15.0
- tensorflow ==1.15.2
- tensorflow-estimator ==1.15.1
- termcolor ==1.1.0
- tqdm ==4.46.0
- urllib3 ==1.25.9
- wrapt ==1.12.1
- zipp ==1.2.0