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Recently, machine learning approaches on Graph data have become very popular.It was observed that significant results were obtained by including implicit orexplicit logical connections between data samples that make up the data to themodel. In this context, the developing GCN model has made significantexperimental contributions with Convolution filters applied to graph data. Thismodel follows Transductive and Semi-Supervised Learning approach. Due to itstransductive property, all of the data samples, which is partially labeled, aregiven as input to the model. Labeling, which is a cost, is very important.Within the scope of this study, the following research question is tried to beanswered: If at least how many samples are labeled, the optimum model successis achieved? In addition, some experimental contributions have been made on theaccuracy of the model, whichever sampling approach is used with fixed labelingeffort. According to the experiments, the success of the model can be increasedby using the local centrality metric.

Exploiting Transductive Property of Graph Convolutional Neural Networks with Less Labeling Effort