README.md

In ionet: Network Analysis for Input-Output Tables

ionet

The goal of ionet is to develop network functionalities specialized for the data generated from input-output tables.

Installation

You can install the development version of ionet from GitHub with:

# install.packages("devtools")
devtools::install_github("Carol-seven/ionet")

Function

btw(): betweenness centrality measure that incorporates available node-specific auxiliary information based on strongest path.

dijkstra(): implementation of the Dijkstra’s algorithm to find the shortest paths from the source node to all nodes in the given network.

Data \| Input-Output Tables

| Database | Economies | Years | Sectors | |:-------------------------------------------|:---------:|:---------:|:-------:| | the National Bureau of Statistics of China | China | 2002 | 122 | | | | 2005 | 42 | | | | 2007 | 135 | | | | 2010 | 41 | | | | 2012 | 139 | | | | 2015 | 42 | | | | 2017 | 149 | | | | 2017 | 42 | | | | 2018 | 153 | | | | 2018 | 42 | | | | 2020 | 153 | | | | 2020 | 42 | | OECD Input-Output Tables 2021 edition | China | 1995–2018 | 45 | | OECD Input-Output Tables 2021 edition | Japan | 1995–2018 | 45 |

Recommended Citation

Xiao, S., Yan, J. and Zhang, P. (2022). Incorporating auxiliary information in betweenness measure for input-output networks. Physica A: Statistical Mechanics and its Applications, 607, 128200. DOI.

ionet documentation built on May 29, 2024, 2:29 a.m.