A tensorflow implementation of GCN-LPA

Overview

GCN-LPA

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.

Files in the folder

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

Running the code

$ python main.py

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

Required packages

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
Owner
Hongwei Wang
Graph neural networks, knowledge graphs, recommender systems
Hongwei Wang
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