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

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