Graph Convolutional Networks (GCNs) have recently gained significant attention due to the success of Convolutional Neural Networks in image and language processing, as well as the prevalence of data that can be represented as graphs. However, GCNs are limited by the size of the graphs they can handle and by the oversmoothing problem, which can be caused by the depth or the large receptive field of these networks. Existing approaches address these limitations by leveraging minibatch training paradigm. However, the strategy of selecting subgraphs to form minibatches is a challenging task because of the dependency between nodes. In this work, we propose a general framework for generating minibatches in an effective way that ensures minimal loss of node interdependence information, preserves the original graph properties, and diversifies the samples for the GCN to improve generalization. We test our training process on real-world datasets with several well-known GCN models and demonstrate the improved results compared to existing methods.