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Theheavens 9c28c3009d | 2 years ago | |
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README.md | 2 years ago |
$$
\alpha_{ij} = \frac{exp(LeakyReLU(a^T[Wh_i||Wh_j||W_r r_{\psi(<i,j>)}]))}{\Sigma_{k\in\mathcal{E}}{exp(LeakyReLU(a^T[Wh_i||Wh_k||W_r r_{\psi(<i,k>)}]))}}
$$
$$
h_i^{(l)} = \sigma(\Sigma_{j\in \mathcal{N}i} {\alpha{ij}^{(l)}W^{(l)}h_j^{(l-1)}} + h_i^{(l-1)})
$$
where $h_i$ and $h_j$ is the features of the source and the target node. $r_{\psi(e)}$ is a $d$-dimension embedding for each edge type $\psi(e) \in T_e$.
and Edge residual:
$$
\alpha_{ij}^{(l)} = (1-\beta)\alpha_{ij}^{(l)}+\beta\alpha_{ij}^{(l-1)}
$$
Clone the Openhgnn-DGL
# For node classification task
python main.py -m SimpleHGN -t node_classification -d imdb4MAGNN -g 0 --use_best_config
If you do not have gpu, set -gpu -1.
Number of nodes
movie | 4278 |
---|---|
director | 2081 |
actor | 5257 |
Number of edges
movie-director | 4278 |
---|---|
movie-actor | 12828 |
Number of nodes
author | 4057 |
---|---|
paper | 14328 |
term | 7723 |
venue | 20 |
Number of edges
author-paper | 19645 |
---|---|
paper-term | 85810 |
paper-venue | 14328 |
Evaluation metric: accuracy
Dataset | HGBn-ACM | HGBn-DBLP | imdb4MAGNN | dblp4MAGNN |
---|---|---|---|---|
Macro_f1 | 66.64 | 86.31 | 48.78 | 86.79 |
Micro_f1 | 88.40 | 87.24 | 52.25 | 86.75 |
You can modify the parameters[SimpleHGN] in openhgnn/config.ini.
Yaoqi Liu[GAMMA LAB]
Submit an issue or email to YaoqiLiu@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。
Python Markdown Shell
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