  We describe a method to select the nodes in Graph datasets for training so
that the model trained on the points selected will be be better than the ones
if we select other points for the purpose of training. This is a very important
aspect as the process of labelling the points is often a costly affair. The
usual Active Learning methods are good but the penalty involved with these
methods is that, we need to re-train the model after selecting the nodes in
each iteration of Active Learning cycle. We come up with a method which use the
concept of Graph Centrality to select the nodes for labeling and training
initially and the training is needed to perform only once. We have tested this
idea on three graph datasets - Cora, Citeseer and Pubmed- and the results are
really encouraging.
