HGSL/README.md

# HGSL
Source code of AAAI submission "Heterogeneous Graph Structure Learning for Graph Neural Networks"
# Requirements
## Python Packages
- Python >= 3.6.8
- Pytorch >= 1.3.0
## GPU Memmory Requirements
- ACM >= 8G
- DBLP >=5G
- Yelp >=3G 
# Usage
Take DBLP dataset as an example:
python train.py --dataset='dblp' 

# Guide on Generation of Semantic Embeddings
The semantic embeddings, i.e. $\mathcal{Z}$ in the paper, are generated by metapath2vec algorithm. Users may refer to https://github.com/dmlc/dgl/tree/master/examples/pytorch/metapath2vec for an implementation.
Initial commit 2020-12-19 17:28:56 +08:00			`# HGSL`
			`Source code of AAAI submission "Heterogeneous Graph Structure Learning for Graph Neural Networks"`
			`# Requirements`
			`## Python Packages`
			`- Python >= 3.6.8`
			`- Pytorch >= 1.3.0`
			`## GPU Memmory Requirements`
			`- ACM >= 8G`
			`- DBLP >=5G`
			`- Yelp >=3G`
			`# Usage`
			`Take DBLP dataset as an example:`
			`python train.py --dataset='dblp'`
Update README.md 2021-06-07 23:23:09 +08:00
			`# Guide on Generation of Semantic Embeddings`
			`The semantic embeddings, i.e. $\mathcal{Z}$ in the paper, are generated by metapath2vec algorithm. Users may refer to https://github.com/dmlc/dgl/tree/master/examples/pytorch/metapath2vec for an implementation.`