This repository is the implementation of GCN-LPA (arXiv):

Unifying Graph Convolutional Neural Networks and Label Propagation
Hongwei Wang, Jure Leskovec
arXiv Preprint, 2020

GCN-LPA is an end-to-end model that unifies Graph Convolutional Neural Networks (GCN) and Label Propagation Algorithm (LPA) for adaptive semi-supervised node classification.

• data/
  • citeseer/
  • cora/
  • pubmed/
  • ms_academic_cs.npz (Coauthor-CS)
  • ms_academic_phy.npz (Coauthor-Phy)
• src/: implementation of GCN-LPA.

$ python main.py

Note: The default dataset is Citeseer. Hyper-parameter settings for other datasets are provided in main.py.

The code has been tested running under Python 3.6.5, with the following packages installed (along with their dependencies):

• tensorflow == 1.12.0
• networkx == 2.1
• numpy == 1.14.3
• scipy == 1.1.0
• sklearn == 0.19.1
• matplotlib == 2.2.2