Corresponding Author


Document Type

Original Article

Subject Areas

Computer Science


Graph Neural Network, Node Classification, homophily and heterophily datasets, Centrality Measures, Feature Selection


One of the most recent developments in the fields of deep learning and machine learning is Graph Neural Networks (GNNs). GNNs core task is the feature aggregation stage, which is carried out over the node's neighbours without taking into account whether the features are relevant or not. Additionally, the majority of these existing node representation techniques only consider the network's topology structure while completely ignoring the centrality information. In this paper, a new technique for explaining graph features depending on four different feature selection approaches and centrality measures in order to identify the important nodes and relevant node features is proposed. Moreover, a significant design approach for graph neural networks is presented. For which, batch normalization is adopted to normalize over the GNN layers. In this study, we primarily focused on homogeneous graph datasets focusing specifically on homophily and heterophily characteristics. The simulation results show that, selecting specific subsets of all features and adding additional features based on centrality measure, which are then send to modified the GNN layer, can lead to better performance across a variety of homophily and heterophily datasets. The outcomes demonstrate the proposed method's superiority to recent methods with accuracy values 0.90.65%, 81.23%, 88.27%, 85.93% and 81.23% for our datasets respectively.