Before revision:  We thus call our approach the Network Centrality Approximation using Graph Convolutional Networks (NCA-GCN)model.In contrast to recent related work, the NCA-GCN model requires only the degree centrality of each node in order to predict any other centrality . We show that the NCA-GCN model works well in different node centralities for different network sizes  .
After revision:  We thus call our approach the Network Centrality Approximation using Graph Embedding (NCA-GE), receives the adjacency matrix of a graph and the degree of every node as input and computes the approximate desired centrality rank for every node. NCA-GE has a time complexity of O(|E|), E being the set of edges of a graph, making it suitable for large networks. NCA-GE also trains pretty fast, requiring only a set of small synthetic graphs, and it works well for different node centralities for different network sizes .