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README.md | 2 years ago |
Paper: MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning
The author did not provide codes. So, we complete it according to the implementation of GTN.
Clone the Openhgnn-DGL
python main.py -m MHNF -t node_classification -d acm4GTN -g 0 --use_best_config
If you do not have gpu, set -gpu -1.
acm4GTN/imdb4GTN
Node classification
Node classification | acm4GTN | imdb4GTN |
---|---|---|
paper | 93.15 | 59.52 |
OpenHGNN | 92.64 | 62.52 |
The model is trained in semi-supervisied node classification.
Supported dataset: acm4GTN, imdb4GTN
Note: Every node in dataset should have the same features dimension.
We process the acm dataset given by HAN. It saved as dgl.heterograph and can be loaded by dgl.load_graphs
You can download the dataset by
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
Or run the code mentioned above and it will download automatically.
num_channels = 2 # number of channel
num_layers = 3 # number of layer
adaptive_lr_flag = True # use different learning rate for weight in HMAELayer.
identity = False # don't need identity because of layers aggregation
Best config can be found in best_config
MHNF model is similar to GTN.
The method HMAEConv to product hybrid relationship matrix is same to GTConv .
MHNF use layer attention to aggregate different hop layer representation thus don't need to add identity to relation matrix set like GTN.
MHNF use channel attention to replace channel aggregation operation in GTN and channel attention can be known as one of channel aggregation operation.
dgl.adj_product_graph which is equivalent SpSpMM.
Fengqi Liang[GAMMA LAB]
Submit an issue or email to lfq@bupt.edu.cn.
OpenHGNN是由北邮GAMMA Lab开发的基于PyTorch和DGL的开源异质图神经网络工具包。
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