论文标题
基于邻接矩阵的更快算法用于中心性
A Faster Algorithm for Betweenness Centrality Based on Adjacency Matrices
论文作者
论文摘要
中心性在复杂的网络分析中至关重要。它表征了网络中节点和边缘的重要性。这是一个至关重要的问题，它可以更快地计算大型网络中的中间性，这迫切需要解决。我们提出了一种基于邻接矩阵的平行计算的新型算法，以用于中心性，该计算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的节点数量有关，而不是边缘数。实验证据表明，该算法是有效有效的。这种新颖的算法比在小型和密集网络上的布兰德斯算法要快，并且为大规模复杂网络上的相互作用指数计算提供了出色的解决方案。
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.
