Centrality measures have been proved to be a salient computational science tool for analyzing networks in the last two to three decades aiding many problems in the domain of computer science, economics, physics, and sociology. With increasing complexity and vividness in the network analysis problems, there is a need to modify the existing traditional centrality measures. Weighted centrality measures usually consider weights on the edges and assume the weights on the nodes to be uniform. One of the main reasons for this assumption is the hardness and challenges in mapping the nodes to their corresponding weights. In this paper, we propose a way to overcome this kind of limitation by hybridization of the traditional centrality measures. The hybridization is done by taking one of the centrality measures as a mapping function to generate weights on the nodes and then using the node weights in other centrality measures for better complex ranking.

中文翻译：

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节点加权中心性：中心性杂交的新方法

在过去的两到三十年中，集中度度量已被证明是分析网络的重要计算科学工具，可解决计算机科学，经济学，物理学和社会学领域的许多问题。随着网络分析问题的复杂性和生动性的增加，需要修改现有的传统集中度度量。加权中心度度量通常考虑边缘上的权重，并假定节点上的权重是均匀的。这种假设的主要原因之一是将节点映射到其相应权重的难度和难度。在本文中，我们提出了一种通过混合传统集中度措施来克服这种限制的方法。