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BM-GCN

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The code of paper "Block Modeling-Guided Graph Convolutional Neural Networks".

// repository documentation

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# Block Modeling-Guided Graph Convolutional Neural Networks
This repository contains the demo code of the paper:
>[Block Modeling-Guided Graph Convolutional Neural Networks]()

which has been accepted by *AAAI2022*.
## Dependencies
* Python3.7
* NumPy
* SciPy
* PyTorch
* TensorFlow.keras
## Example Usages
Before running the code， please unzip the *data_geom.zip* and make a directory named *checkpoint*.

* `python main.py --dataset cora --enhance 3.0 --self_loop 1.5`
* `python main.py --dataset citeseer --enhance 4.0 --self_loop 2.0`
* `python main.py --dataset pubmed --enhance 2.0 --self_loop 3.0`
* `python main.py --dataset squirrel --enhance 2.0 --self_loop 0.0`
* `python main.py --dataset chameleon --enhance 0.8 --self_loop 0.0`
* `python main.py --dataset texas --num_gcn_layers 2 --enhance 1.0 --self_loop 0.0`

Please refer to the *args.py* for more parameters.
## Reference
If you make advantage of BM-GCN in your research, please cite the following in your manuscript:

Dongxiao He, et al. "Block Modeling-Guided Graph Convolutional Neural Networks." In AAAI. 2022.
## License
Tianjin University

BM-GCN

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