Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification

Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification. SIGIR(short paper), 2020.

Overview

We provide a pytorch implementation of LCGNN. Graph neural networks (GNNs) achieve remarkable success in graphbased semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node. The success of GNNs at node classification depends on the assumption that connected nodes tend to have the same label. However, such an assumption does not always work, limiting the performance of GNNs at node classification. We propose label-consistency based graph neural network (LC-GNN), leveraging node pairs unconnected but with the same labels to enlarge the receptive field of nodes in GNNs.

Requirements

the script has been tested running under Python 3.7, with the following packages installed (along with their dependencies):

• torch
• torch_geometric
• numpy
• scipy

Run the LCGCN

• cd gcn_run
• python lcgcn_ld.py

Run the LCGAT

• cd gat_run
• python lcgcn_ld.py

Parameters

• --lambda2 FLOAT weight of regulariztion loss.

Acknowledgement

We implement our model based on torch_geometric