# HGCN

**Repository Path**: milo7hao/HGCN

## Basic Information

- **Project Name**: HGCN
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-09-06
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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>`.