Description
Generalized Path Analysis for Social Networks.
Description
The social network literature features numerous methods for assigning value to paths as a function of their ties. 'gretel' systemizes these approaches, casting them as instances of a generalized path value function indexed by a penalty parameter. The package also calculates probabilistic path value and identifies optimal paths in either value framework. Finally, proximity matrices can be generated in these frameworks that capture high-order connections overlooked in primitive adjacency sociomatrices. Novel methods are described in Buch (2019) <https://davidbuch.github.io/analyzing-networks-with-gretel.html>. More traditional methods are also implemented, as described in Yang, Knoke (2001) <doi:10.1016/S0378-8733(01)00043-0>.
README.md
gretel: Generalized Path Analysis for Social Networks
Features methods for quantifying path values and identifying optimal paths under a variety of modeling assumptions. Intended to be used in service of other structural analyses.
Getting Started
Installation Instructions
library(devtools)
install_github("davidbuch/gretel")
Example
# Identify the path of optimal conductivity between nodes 1 and 5 of a sociomatrix
# Using example data from *Yang, Knoke* (2001) <DOI: 10.1016/S0378-8733(01)00043-0>
best_path <- opt_gpv(YangKnoke01, source = 1, target = 5, alpha = 1)
# Compare the conductivity of this path to that of an inferior path
gpv(YangKnoke01, path = best_path, alpha = 1)
gpv(YangKnoke01, path = c(1,2,3,4,5), alpha = 1)
Please see the package vignette for more information and examples.
Overview
This package contains two categories of functions. The first category is concerned with assigning values to user specified paths, while the second identifies paths of optimal value.
Key functions in the path value calculation category are
gpv, which calculates Generalized Path Valueppv, which calculates Probabilistic Path Valuebinary_distance,peay_path_value,flament_path_length,peay_average_path_value, andflament_average_path_length, which calculate path value measures described in Yang, Knoke (2001).generate_proximities, which generates a matrix of values representing the measures of optimal paths from each source node (row index) to each target node (column index).
Key functions in the optimal path identification category are
opt_gpv, which identifies the path of optimal Generalized Path Value from a particular source node to a particular target nodeopt_ppv, which identifies the path of optimal Probabilistic Path Value from a particular source node to a particular target nodeall_opt_gpv, which identifies the 'gpv'-optimal paths from every source node to every target nodeall_opt_ppv, which identifies the 'ppv'-optimal paths from every source node to every target nodeunpack, which unpacks the Dijkstra-format encoded shortest paths returned byall_opt_gpvandall_opt_ppv. See their help pages for details.
Author
David A. Buch
Citation
TBA
Acknowledgements
To my Dad, on his birthday.
License
GPL-3