The goal of ionet is to develop network functionalities specialized for the data generated from input-output tables.
You can install the development version of ionet from GitHub with:
# install.packages("devtools")
devtools::install_github("Carol-seven/ionet")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.
| 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 |
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.