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README.md | 2 years ago |
Clone the Openhgnn-DGL
python main.py -m HGNN_AC -t node_classification -d imdb4MAGNN -g 0
If you do not have gpu, set -gpu -1.
the dataset imdb4MAGNN is supported.
Node classification | Macro-F1 | Micro-F1 |
---|---|---|
MAGNN | 58.65% | 59.20% |
paper | 60.75% | 60.98% |
OpenHGNN | 60.54% | 60.70% |
The perform of experiments are run in the setting of paper which uses SVM classification, so it is a little bit different from semi-supervised node classification. And directly running the model is using semi-supervised node classification trainerflow.
Number of nodes
movie | 4278 |
---|---|
director | 2081 |
actor | 5257 |
Number of edges
movie-director | 4278 |
---|---|
movie-actor | 12828 |
Types of metapaths: MDM, MAM, DMD, DMAMD, AMA, AMDMA
. Please note that the M
is movie, D
is director, A
is actor, and the edges above are all bidirectional.
[TODO]
Graph preprocess
Attribute Completion with Attention Mechanism
Dropping some Attributes
Combination with the HIN Model
You can modify the parameters in openhgnn/config.ini
feats_drop_rate = 0.3 # feature drop rate to get the feature drop list
attn_vec_dim = 64 # the dimesions of vector in the Attention Layer
feats_opt = 110 # the type of nodes that needs to get the new features
loss_lambda = 0.2 # the weighted coefficient to balance the two parts.
src_node_type = 2 # the type of nodes that has the raw attributes
dropout = 0.1 # the drop rate used in Drop some Attributes
num_heads = 8 # the num of heads used in muti-head attention mechanism
HIN = MAGNN # the type of model used in Combination with the HIN Model.
Yaoqi Liu[GAMMA LAB]
Submit an issue or email to YaoqiLiu@bupt.edu.cn.
This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL.
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