An efficient scheme based on graph centrality to select nodes for training for effective learning

TL;DR

This paper introduces a novel graph centrality-based node selection method for training machine learning models efficiently, reducing labeling costs and improving model performance on graph datasets.

Contribution

The paper proposes a new graph centrality-based node selection scheme with both a single-training and an active learning approach, enhancing training efficiency and effectiveness.

Findings

Effective node selection improves model accuracy

Method reduces labeling effort and costs

Results on Cora, Citeseer, Pubmed datasets are promising

Abstract

The process of selecting points for training a machine learning model is often a challenging task. Many times, we will have a lot of data, but for training, we require the labels and labeling is often costly. So we need to select the points for training in an efficient manner so that the model trained on the points selected will be better than the ones trained on any other training set. We propose a novel method to select the nodes in graph datasets using the concept of graph centrality. Two methods are proposed - one using a smart selection strategy, where the model is required to be trained only once and another using active learning method. We have tested this idea on three popular graph datasets - Cora, Citeseer and Pubmed- and the results are found to be encouraging.