Graph Convolutional Network Design for Node Classification Accuracy Improvement

Abstract

Graph convolutional networks (GCNs) provide an advantage in node classification tasks for graph-related data structures. In this paper, we propose a GCN model for enhancing the performance of node classification tasks. We design a GCN layer by updating the aggregation function using an updated value of the weight coefficient. The adjacency matrix of the input graph and the identity matrix are used to calculate the aggregation function. To validate the proposed model, we performed extensive experimental studies with seven publicly available datasets. The proposed GCN layer achieves comparable results with the state-of-the-art methods. With one single layer, the proposed approach can achieve superior results.