A parallel closed centrality algorithm for complex networks

K. Erciyes
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Abstract

Complex networks are large and analysis of these networks require significantly different methods than small networks. Parallel processing is needed to provide analysis of these networks in a timely manner. Graph centrality measures provide convenient methods to assess the structure of these networks. We review main centrality algorithms, describe implementation of closed centrality in Python and propose a simple parallel algorithm of closed centrality and show its implementation in Python with obtained results.
复杂网络的并行封闭中心性算法
复杂网络规模很大,分析这些网络所需的方法与小型网络有很大不同。为了及时地对这些网络进行分析,需要并行处理。图中心性度量为评估这些网络的结构提供了方便的方法。我们回顾了主要的中心性算法,描述了封闭中心性在Python中的实现,提出了一个简单的并行封闭中心性算法,并给出了它在Python中的实现和得到的结果。
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