/LabelPropagation-dgl

A DGL implementation of "Learning from Labeled and Unlabeled Data with Label Propagation".

Primary LanguagePython

DGL Implementation of Label Propagation

This DGL example implements the method proposed in the paper Learning from Labeled and Unlabeled Data with Label Propagation.

Contributor: xnuohz

Requirements

The codebase is implemented in Python 3.7. For version requirement of packages, see below.

dgl 0.6.0.post1
torch 1.7.0

The graph datasets used in this example

The DGL's built-in Cora, Pubmed and Citeseer datasets. Dataset summary:

Dataset #Nodes #Edges #Feats #Classes #Train Nodes #Val Nodes #Test Nodes
Citeseer 3,327 9,228 3,703 6 120 500 1000
Cora 2,708 10,556 1,433 7 140 500 1000
Pubmed 19,717 88,651 500 3 60 500 1000

Usage

# Cora
python main.py

# Citeseer
python main.py --dataset Citeseer --num-layers 100 --alpha 0.99

# Pubmed
python main.py --dataset Pubmed --num-layers 60 --alpha 1

Performance

Dataset Cora Citeseer Pubmed
Results(DGL) 69.20 51.30 71.40