dgl/examples/pytorch/ogc

• Optimized Graph Convolution (OGC)
• • Example Implementor
  • Dependencies
  • Dataset
  • Usage
  • Performance

Optimized Graph Convolution (OGC)

This DGL example implements the OGC method from the paper: From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited.
With only one trainable layer, OGC is a very simple but powerful graph convolution method.

Example Implementor

This example was implemented by Sinuo Xu when she was an undergraduate at SJTU.

Dependencies

Python 3.11.5
PyTorch 2.0.1
DGL 1.1.2
scikit-learn 1.3.1

Dataset

The DGL's built-in Cora, Pubmed and Citeseer datasets, as follows:

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

python main.py --dataset cora
python main.py --dataset citeseer
python main.py --dataset pubmed

Performance

Dataset	Cora	Citeseer	Pubmed
OGC (DGL)	86.9(±0.2)	77.4(±0.1)	83.6(±0.1)
OGC (Reported)	86.9(±0.0)	77.4(±0.0)	83.4(±0.0)