节点加权中心性:一种新的中心性杂交方法

Q1 Mathematics

Computational Social Networks Pub Date : 2020-11-13 DOI:10.1186/s40649-020-00081-w

Anuj Singh, Rishi Ranjan Singh, S. R. S. Iyengar

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引用次数: 22

摘要

在过去的二三十年里，中心性度量已经被证明是一个重要的计算科学工具，用于分析网络，帮助解决计算机科学、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，有必要对现有的传统中心性度量进行修正。加权中心性度量通常考虑边缘上的权值，并假设节点上的权值是均匀的。这种假设的主要原因之一是将节点映射到相应权重的难度和挑战。本文提出了一种克服这种局限性的方法，即对传统中心性测度进行杂交。杂交是通过将其中一个中心性度量作为映射函数来生成节点上的权值，然后使用其他中心性度量中的节点权值来实现更好的复杂排序。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Node-weighted centrality: a new way of centrality hybridization

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.

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来源期刊

Computational Social Networks Mathematics-Modeling and Simulation

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.