Generating General Preferential Attachment Networks with R Package wdnet

TL;DR

This paper introduces the R package wdnet for efficient, flexible generation of large-scale weighted, directed preferential attachment networks, enhancing existing tools with new features and performance improvements.

Contribution

The paper presents a new R package that enables flexible, efficient generation of general PA networks with features not available in existing software.

Findings

wdnet is efficient for large-scale PA network generation

Provides additional features like multiple edges and heterogeneous reciprocal edges

Offers comparable efficiency to existing packages in restricted settings

Abstract

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.