HGCN: A Heterogeneous Graph Convolutional Network-Based Deep Learning Model Toward Collective Classification
- Python 2.7
- TensorFlow 1.14.0
Here, we provide two real-world HIN datasets: CORA and IMDB.
Run HGCN training on the CORA dataset:
$ python train.py --dataset cora --kernel-size 4 --inception-depth 1 --label-propagation 0 --epochs 30
Run HGCN training on the IMDB dataset:
$ python train.py --dataset imdb --kernel-size 2 --inception-depth 1 --label-propagation 0 --epochs 30
If you want to train HGCN on your own dataset, you should prepare the following four files:
- *.adj.npz: The adjacency matrix for each type of edges.
- *.feat.label.npz: The one-hot codes of the labels of target-type nodes. Note that, 0 to initialize the features of nontarget-type nodes.
- *.label.all: The labels of all target-type nodes. Each line contains one token
<label>. - *.label.part: the target-type nodes that have the labels, and their labels. Each line contains two token
<node> <label>.