Awesome
SAGE
A tensorflow implementation of self attentive graph embedding (SAGE) in Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019)
<p align="center"> <img width="800" src="sage.JPG"> </p> <p align="justify">Details can be found in the paper:
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective. Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, Junzhou Huang. WWW, 2019. [Paper]
Requirements
python 2.7.15
tensorflow 1.90
numpy 1.15.0
networkx 2.1
scipy 1.1.0
sklearn 0.19.1
Dataset
proteins
Model options
--epochs INT Number of epochs. Default is 17.
--weight-decay FLOAT Weight decay of Adam. Defatul is 5*10^-4.
--gamma FLOAT Regularization parameter. Default is 0.19.
--learning-rate FLOAT Adam learning rate. Default is 0.01.
Example
use pretrained model
python train.py
train from scratch
python train.py --train True
Result
The average accuracy for the pretrained model is 0.80328 for proteins.