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：介数中心性是复杂网络分析中的重要指标，用于表征网络中节点和边的重要性。如何在大规模网络中更快速地精确计算介数中心性是一个亟待解决的关键问题。我们提出了一种基于邻接矩阵并行计算的新型介数中心性算法，该算法在处理大规模网络时比现有算法速度更快。该算法的时间复杂度仅与网络中的节点数量相关，而与边数无关。实验证据表明，该算法高效且有效。在小规模密集网络上，该新型算法比Brandes算法速度更快，并为大规模复杂网络的介数中心性指标计算提供了优秀解决方案。