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.

中文翻译：

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使用节点嵌入和机器学习近似网络中心性度量

由于当今网络规模庞大，从现实世界网络中提取信息已成为一项关键挑战。根据预期的节点中心性，由于计算成本，为如此大型的复杂网络计算它变得不可行。解决这个问题的一种方法是开发能够近似网络中心性的快速方法。在本文中，我们提出了一种使用神经网络和图嵌入技术有效逼近大型网络节点中心性的方法。我们提出的模型名为 Network Centrality Approximation using Graph Embedding (NCA-GE)，它接收图的邻接矩阵和每个节点的度数作为输入，并计算每个节点的近似期望中心度等级。NCA-GE 的时间复杂度为 O(|E|)，E 是图的边集，使其适用于大型网络。NCA-GE 的训练速度也非常快，只需要一组小的合成图，并且适用于不同的节点中心性和不同的网络规模和拓扑结构。最后，我们将我们的方法与使用度和特征向量中心性作为输入来近似中心性排名的最先进方法进行比较，我们表明 NCA-GE 在各种场景中都优于前者。