Extracting information from real-world networks has become a key challenge due to the large sizes such networks achieve nowadays. Depending on the intended node centrality, it becomes unfeasible to compute it for such large complex networks due to the computational cost. One way to tackle this problem is by developing fast methods capable of approximating network centralities. In this paper , we propose an approach for efficiently approximating node centralities for large networks using Neural Networks and Graph Embedding techniques. Our proposed model, entitled 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 and different network sizes and topologies. Finally, we compare our approach to the state-of-the-art method that approximates centrality ranks using the degree and eigenvector centralities as input , where we show that the NCA-GE outperforms the former in a variety of scenarios .
Extracting information from real-world large networks is a key challenge nowadays. Depending on the intended node centrality, it becomes unfeasible to compute it for such large networks given the computational cost. One solution is to develop fast methods capable of approximating network centralities. Here , we propose an approach for efficiently approximating node centralities for large networks using Neural Networks and Graph Embedding techniques. Our proposed model, entitled Network Centrality Approximation using Graph Embedding (NCA-GE), receives as input the adjacency matrix of a graph and a set of features for each node (here, we use only the degree) 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 a thousand small synthetic scale-free graphs (ranging from 100 to 1000 nodes each) , and it works well for different node centralities , network sizes, and topologies. Finally, we show that NCA-GE outperforms, in a variety of scenarios, the state-of-the-art method that approximates centrality ranks using the degree and eigenvector centralities as input .
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