Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching

TL;DR

This paper introduces GLDGCN, a novel graph convolutional neural network with dual layers and graph learning, enhancing semi-supervised node classification accuracy on multiple datasets, including large graphs, through subgraph sketching and clustering techniques.

Contribution

The paper proposes GLDGCN with dual convolutional and graph learning layers, and a scalable semi-supervised classification method using subgraph sketching for large graphs.

Findings

GLDGCN outperforms baseline methods on citation networks.

The scalable algorithm effectively processes large graphs like PPI and Reddit.

Hyperparameter and network depth analysis improves model performance.

Abstract

In this paper, we propose the Graph-Learning-Dual Graph Convolutional Neural Network called GLDGCN based on the classic Graph Convolutional Neural Network(GCN) by introducing dual convolutional layer and graph learning layer. We apply GLDGCN to the semi-supervised node classification task. Compared with the baseline methods, we achieve higher classification accuracy on three citation networks Citeseer, Cora and Pubmed, and we also analyze and discussabout selection of the hyperparameters and network depth. GLDGCN also perform well on the classic social network KarateClub and the new Wiki-CS dataset. For the insufficient ability of our algorithm to process large graphs during the experiment, we also introduce subgraph clustering and stochastic gradient descent methods into GCN and design a semi-supervised node classification algorithm based on the CLustering Graph Convolutional neural Network, which enables GCN to process large graph and improves its application value. We complete semi-supervised node classification experiments on two classic large graph which are PPI dataset (more than 50,000 nodes) and Reddit dataset (more than 200,000 nodes), and also perform well.