/HGCN

Primary LanguagePython

HGCN

HGCN: A Heterogeneous Graph Convolutional Network-Based Deep Learning Model Toward Collective Classification

Prerequisites

  • Python 2.7
  • TensorFlow 1.14.0

Getting Started

Default Run & Parameters

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

Training on your own datasets

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>.