- paper: Heterogeneous Graph Neural Network via Attribute Completion
- Code from author: HGNN_AC
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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.
- Device: CPU, Intel(R) Core(TM) i7-9750H CPU @ 2.60GHz 2.59 GHz
- Dataset: IMDB
| 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.
- We process the IMDB dataset given by MAGNN. It is saved as dgl.heterograph and can be loaded by dgl.load_graphs
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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 theMis movie,Dis director,Ais actor, and the edges above are all bidirectional.
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[TODO]
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Graph preprocess
- To get some information from the original graph. It divides the nodes with attributes into a feature keep list and a feature drop list. It also uses the edge information to get the nodes' adjacency matrices related to the source nodes.
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Attribute Completion with Attention Mechanism
- HGNN-AC adopts a masked attention mechanism which means we only calculate
$e_{vu}$ for nodes$u\in{N_v^+}$ , where$u\in{N_v^+}$ denotes the first-order neighbors of node$v$ in set$V^+$ , where$V^+$ is the set of nodes with attributes. - Then, softmax function is applied to get normalized weighted coefficient
$a_{vu}$ . - HGNN-AC can perform weighted aggregation of attributes for node
$v$ according to weighted coefficient$a_{vu}$ : - Specially, the attention process is extended to a multi-head attention to stabilize the learning process and reduce the high variance
- HGNN-AC adopts a masked attention mechanism which means we only calculate
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Dropping some Attributes
- HGNN-AC drops some attributes according to the feature drop list. It uses the Attribute Completion to get the new features of the nodes and compares the new features with the original features.
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Combination with the HIN Model
- Finally, we can apply the proposed framework to MAGNN model or some other models. For more details, you can refer to the corresponding models.
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 [email protected].