Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.
Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran.
Bioinformatics. 2019 Apr 15;35(8):1436-1437. doi: 10.1093/bioinformatics/bty819.
Centrality analysis involves a series of ambiguities in that there are numerous well-known centrality measures with differing algorithms for establishing which nodes in a network are essential. There is no clearly preferred measure or means of deciding which measure is most germane to a given network with respect to node essentiality vis-à-vis topological features. Our aim here was to develop an instrument that enables comparisons among potentially appropriate centrality measures to be made with respect to network structure and thereby to support the identification of the most informative measure according to dimensional reduction methods.
The Central Informative Nodes in Network Analysis (CINNA) package introduced herein gathers all required functions for centrality analysis in weighted/unweighted and directed/undirected networks. Then, it compares, assorts and visualizes centrality measures to select which best describes the node importance.
CINNA is available in CRAN, including a tutorial. URL: https://cran.r-project.org/web/packages/CINNA/index.html.
中心性分析涉及一系列的歧义，因为有许多著名的中心性度量标准，它们的算法不同，用于确定网络中的哪些节点是至关重要的。目前还没有明确的首选度量标准或方法来决定对于给定的网络，哪种度量标准与节点的拓扑特征相比，与节点的重要性最相关。我们的目标是开发一种工具，使人们能够根据网络结构对潜在合适的中心性度量标准进行比较，从而根据降维方法支持识别最具信息量的度量标准。
本文引入的网络分析中的中心信息节点（Central Informative Nodes in Network Analysis，CINNA）包汇集了加权/无权重和有向/无向网络中所有中心性分析所需的功能。然后，它比较、整理和可视化中心性度量标准，以选择最能描述节点重要性的度量标准。
CINNA 可在 CRAN 上获得，包括一个教程。网址：https://cran.r-project.org/web/packages/CINNA/index.html。
