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(WAIM/APWeb 2023)Hypergraph-Enhanced Self-supervised Heterogeneous Graph Representation Learning

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HHGR

Requirements

Dependencies (with python >= 3.7):

numpy==1.21.2
torch==1.10.1
torch-cluster==1.5.9                  
torch-geometric==2.0.4                  
torch-scatter==2.0.9                   
torch-sparse==0.6.13                

Preprocessing

Dataset

Create a folder data to store source data files.

Prepocess the data

Generate the postive samples:

python pos.py

Generate the hyperedges by using hyperedge.ipynb.

Training

training with default configuration:

# on DBLP dataset
python main_hyper.py --dataset DBLP

# on ACM dataset
python main_hyper.py --dataset ACM

# on Yelp dataset
python main_hyper.py --dataset Yelp

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(WAIM/APWeb 2023)Hypergraph-Enhanced Self-supervised Heterogeneous Graph Representation Learning

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  • Python 87.4%
  • Jupyter Notebook 12.6%