One can run training on the main HNHN script as follows:
E.g. specify dataset name, set random seed, and number of layers: python hypergraph.py --dataset_name dblp --seed --n_layers 2
E.g. use a dimension-reduced version of the dataset: python hypergraph.py --dataset_name cora --do_svd
The functions that call on baseline repository codes are in baseline.py, and can be called as follows:
python baseline.py --dataset_name citeseer --method hgnn
For all runtime options, please see either utils.py or run:
python hypergraph.py --h
More examples:
python hypergraph.py --dataset_name citeseer --seed python hypergraph.py --dataset_name dblp --seed --n_layers 2 python hypergraph.py --dataset_name cora --seed --do_svd
#to test alpha/beta, adjust code for alpha range python hypergraph.py --dataset_name citeseer --seed
#to predict edges python hypergraph.py --dataset_name citeseer --seed --predict_edge emacs hypergraph.py --seed --predict_edge --dataset_name citesser
#Experiment with edge linear: python hypergraph.py --dataset_name cora --seed --edge_linear
python hypergraph.py --seed --predict_edge --dataset_name citeseer --n_layers 1
To run baselines: python baselines.py python baseline.py --predict_edge --dataset_name citeseer --method hgnn python baseline.py --dataset_name pubmed --method hgnn python baseline.py --dataset_name dblp --method hgnn
python baseline.py --dataset_name cora --do_svd --method hgnn