Multi-grained Semantics-aware Graph Neural Networks

Implementation of the AdamGNN with Pytorch, another implementation with Tensorflow incoming.

Required packages

The code has been tested running under Python 3.7.1. with the following packages installed (along with their dependencies):

• numpy == 1.18.1
• pandas == 1.0.3
• scikit-learn == 0.22.2
• networkx == 2.4
• pytorch == 1.4.0
• torch_geometric == 1.4.2

Data requirement

All eight datasets we used in the paper are all public datasets which can be downloaded from the internet.

Code execution

Two demo file is given to show the execution of link prediction (LP) and node classification (NC) tasks.

Citation

Please cite our paper if you make use of this code in your own work:

@article{ZLP221,
author = {Zhiqiang Zhong and Cheng{-}Te Li and Jun Pang},
title = {Multi-grained Semantics-aware Graph Neural Networks},
journal = {IEEE Transactions on Knowledge and Data Engineering (TKDE)},
year = {2022},
}