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HAN[WWW2019]

Paper: Heterogeneous Graph Attention Network

Code from author: https://github.com/Jhy1993/HAN

Code from dgl Team: https://github.com/dmlc/dgl/tree/master/examples/pytorch/han

We use the code from dgl Team and integrate the model into our package.

How to run

Clone the Openhgnn-DGL

python main.py -m HAN -t node_classification -d acm_han_raw -g 0

If you do not have gpu, set -gpu -1.

Candidate dataset: acm_han_raw, acm4GTN, imdb4GTN

Performance

acm_han_raw Macro-F1 Micro-F1
HAN[OpenHGNN] 90.11 90.02
Node classification acm4GTN imdb4GTN
GTN[OpenHGNN] 92.22 61.58
HAN[OpenHGNN] 91.63 57.30

TrainerFlow: node_classification

The model is trained in semi-supervisied node classification.

Model

We implement HANLayer with MetapathConv.

SemanticAttention

Dataset

Supported dataset: acm_han_raw, acm4GTN, imdb4GTN

You can download the dataset by

wget https://s3.cn-north-1.amazonaws.com.cn/dgl-data/dataset/acm_han_raw.zip
wget https://s3.cn-north-1.amazonaws.com.cn/dgl-data/dataset/acm4GTN.zip
wget https://s3.cn-north-1.amazonaws.com.cn/dgl-data/dataset/imdb4GTN.zip

Hyper-parameter specific to the model

num_channels = 2 # number of channel
num_layers = 3 # number of layer
adaptive_lr_flag = True # use different learning rate for weight in GTLayer.

Best config can be found in best_config

Related API in DGL

dgl.metapath_reachable_graph

GATConv

More

Contirbutor

Tianyu Zhao[GAMMA LAB]

If you have any questions,

Submit an issue or email to [email protected].