Skip to content

Latest commit

 

History

History

HPN

Folders and files

NameName
Last commit message
Last commit date

parent directory

..
 
 

HPN[TKDE2021]

Paper: Heterogeneous Graph Propagation Network

The author did not provide codes. So, we complete it according to the implementation of HAN

How to run

Clone the Openhgnn-DGL

python main.py -m HPN -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

HPN[OpenHGNN] Macro-F1 Micro-F1
acm_han_raw 91.80 91.80
acm4GTN 91.04 90.92
imdb4GTN 60.96 64.00

TrainerFlow: node_classification

The model is trained in semi-supervisied node classification.

model

HPNLayer

SemanticFusion

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

k_layer = 1 # iterations in Semantic Propagation
alpha = 0.1 # Value of restart probability
out_embedsize = 64 # Dimension of the final embedding
edge_drop = 0 # the dropout rate on edges that controls the messages received by each node

Best config can be found in best_config

Related API in DGL

dgl.metapath_reachable_graph

APPNPConv

More

Contirbutor

Donglin Xia[GAMMA LAB]

If you have any questions,

Submit an issue or email to [email protected].