Betweenness centrality is essential in complex network analysis; it characterizes the importance of nodes and edges in networks. It is a crucial problem that exactly computes the betweenness centrality in large networks faster, which urgently needs to be solved. We propose a novel algorithm for betweenness centrality based on the parallel computing of adjacency matrices, which is faster than the existing algorithms for large networks. The time complexity of the algorithm is only related to the number of nodes in the network, not the number of edges. Experimental evidence shows that the algorithm is effective and efficient. This novel algorithm is faster than Brandes' algorithm on small and dense networks and offers excellent solutions for betweenness centrality index computing on large-scale complex networks.

翻译：在复杂的网络分析中,中心点之间至关重要;中心点和边缘在网络中的重要性。这是一个关键问题,它精确地计算出在大型网络中中心点之间的中心点,这迫切需要解决。我们建议基于平行计算相邻矩阵的新型中心点算法,这比大型网络的现有算法要快。算法的时间复杂性仅与网络中节点的数量有关,而不是边缘的数量。实验证据表明算法是有效和高效的。这种新奇算法比Brandes在小型和密集网络上的算法更快,并为大型复杂网络中计算中间点指数提供了极好的解决方案。