Node classification with graph neural network based centrality measures and feature selection

Graph neural networks (GNNs) are a new topic of research in data science where data structure graphs are used as important components for developing and training neural networks. GNN always learns the weight importance of the neighbor for perform message aggregation in which the feature vectors of all neighbors are aggregated without considering whether the features are useful or not. Using such more informative features positively affect the performance of the GNN model. So, in this paper i) after selecting a subset of features to define important node features, we present new graph features’ explanation methods based on graph centrality measures to capture rich information and determine the most important node in a network. Through our experiments, we find that selecting certain subsets of these features and adding other features based on centrality measure can lead to better performance across a variety of datasets and ii) we introduce a major design strategy for graph neural networks. Specifically, we suggest using batch renormalization as normalization over GNN layers. Combining these techniques, representing features based on centrality measures that passed to multilayer perceptron (MLP) layer which is then passed to adjusted GNN layer, the proposed model achieves greater accuracy than modern GNN models.