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@ -12,5 +12,9 @@ Source code of AAAI submission "Heterogeneous Graph Structure Learning for Graph
Take DBLP dataset as an example: Take DBLP dataset as an example:
python train.py --dataset='dblp' python train.py --dataset='dblp'
# Guide on Generation of Semantic Embeddings # FAQ
## Code of preprocessing data?
The data is originally preprocessed by GTN project (https://github.com/seongjunyun/Graph_Transformer_Networks).
## How to generate 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. 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.