Generating General Preferential Attachment Networks with R Package wdnet
Generating General Preferential Attachment Networks with R Package wdnet
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DOI：
10.6339/23-jds1110
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发表时间：
2023-01
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通讯作者：
Yelie Yuan;Tiandong Wang;Jun Yan;Panpan Zhang
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作者：
Yelie Yuan;Tiandong Wang;Jun Yan;Panpan Zhang
偏好依恋(PA)网络模型在各个科学学科中有着广泛的应用。大规模PA网络的高效生成有助于揭示其结构属性，并促进相关分析方法的发展。现有的软件包仅为此目的提供有限的功能，配置和效率有限。我们提出了一个通用的，用户友好的实现加权的，有向PA网络生成R包wdnet。核心算法基于一种高效的二叉树方法。该包还允许一次添加多个边、不同种类的倒数边和用户指定的偏好函数。幕后的引擎是用C++实现的。详细说明了该程序包的用法。一项基准研究表明，wdnet对于生成其他程序包所不具备的通用PA网络是有效的。在现有程序包可以处理的受限设置中，wdnet提供了相当的效率。
Preferential attachment (PA) network models have a wide range of applications in various scientific disciplines. Efficient generation of large-scale PA networks helps uncover their structural properties and facilitate the development of associated analytical methodologies. Existing software packages only provide limited functions for this purpose with restricted configurations and efficiency. We present a generic, user-friendly implementation of weighted, directed PA network generation with R package wdnet. The core algorithm is based on an efficient binary tree approach. The package further allows adding multiple edges at a time, heterogeneous reciprocal edges, and user-specified preference functions. The engine under the hood is implemented in C++. Usages of the package are illustrated with detailed explanation. A benchmark study shows that wdnet is efficient for generating general PA networks not available in other packages. In restricted settings that can be handled by existing packages, wdnet provides comparable efficiency.