An efficient scheme based on graph centrality to select nodes for training for effective learning
This addresses the costly labeling issue in graph-based machine learning, though it appears incremental as it builds on existing centrality and active learning concepts.
The paper tackles the problem of efficiently selecting training nodes in graph datasets to reduce labeling costs, proposing a method based on graph centrality that achieves encouraging results on Cora, Citeseer, and Pubmed datasets.
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.