signnet

signnet: An R package for analyzing signed networks - Published in JOSS (2023)

https://github.com/schochastics/signnet

Science Score: 98.0%

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Published in Journal of Open Source Software

Keywords

network-analysis signed-networks sna

Scientific Fields

Earth and Environmental Sciences Physical Sciences - 40% confidence

Last synced: 4 months ago · JSON representation ·

Repository

R package for signed networks

Basic Info

Host: GitHub
Owner: schochastics
License: other
Language: R
Default Branch: main
Homepage: https://schochastics.github.io/signnet/
Size: 6.55 MB

Statistics

Stars: 23
Watchers: 4
Forks: 7
Open Issues: 0
Releases: 6

Topics

network-analysis signed-networks sna

Created over 6 years ago · Last pushed 4 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    fig.path = "man/figures/README-",
    out.width = "100%"
)
```
# signnet 


[![R-CMD-check](https://github.com/schochastics/signnet/workflows/R-CMD-check/badge.svg)](https://github.com/schochastics/signnet/actions)
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[![JOSS](https://joss.theoj.org/papers/10.21105/joss.04987/status.svg)](https://doi.org/10.21105/joss.04987)


The package provides methods to analyse signed networks (i.e. networks with both positive and negative ties).

## Installation

You can install the released version of signnet from CRAN with:

```{r inst, eval=FALSE}
install.packages("signnet")
```

The development version from is available with:

``` {r inst2, eval=FALSE}
# install.packages("devtools")
devtools::install_github("schochastics/signnet")
```

## Structural Balance and Triads

The principles underlying structural balance are based on a theory in social psychology dating back to the work of Heider in the 1940s, which was generalized and extended to graphs by Cartwright and Harary in the 1950s. In its simplest form, it is defined via triangles. A triangle is balanced if
all ties are positive ("the friend of a friend is a friend") or only one tie is positive ("the enemy of my enemy is my friend"). The remaining configurations are said to be unbalanced.



A network is balanced if i.a., it can be partitioned into two vertex subsets, such that intra-group edges are all positive and inter-group edges are all negative. Determining this is easy, but measuring a *degree of balancedness* (i.e. how close is a network to be balanced?) is not. The package, so far, implements three methods to calculate balance scores. All are defined such that a value of one indicates perfect balance and zero perfect unbalance. Though for intermediate networks, results may vary significantly. Check [this paper](https://doi.org/10.1093/comnet/cnx044) by Samin Aref (and his other work) for more details.

```{r balance_example,message=FALSE}
library(igraph)
library(signnet)
data("tribes")

balance_score(tribes, method = "triangles")
balance_score(tribes, method = "walk")
balance_score(tribes, method = "frustration")
```

For directed signed networks, `triad_census_signed()` can be used to compute the count for all 138 non-isomorphic signed triads.
*(The code to reproduce this figure can be found in [this gist](https://gist.github.com/schochastics/dd1974b42cfa5367cf6d8cb9e43bae32))*


# Blockmodeling

The package implements two different blockmodeling algorithms. The classic one tries to partition the 
network into a specified set of groups such that intra group edges are positive and inter group edges are negative.

```{r block_code,message=FALSE}
clu <- signed_blockmodel(tribes, k = 3, alpha = 0.5, annealing = TRUE)
clu
```

The parameter *k* is the number of groups and *alpha* specifies the penalty of negative inter group and positive intra group edges.
If `alpha = 0` (`alpha = 1`) then only positive inter group (negative intra group) edges are penalized. Set `alpha = 0.5` for equal penalization
The algorithm is not exact and just a heuristic. If `annealing = TRUE`, then simulated annealing is used. This improves the result, but may take additional time.

The result of the blockmodel can be visualized with `ggblock` (requires `ggplot2`)
```{r block_example}
ggblock(tribes, clu$membership, show_blocks = TRUE, show_labels = TRUE)
```


The second blockmodeling technique is known as *generalized blockmodeling*. This method removes the restriction of positve (negative) inter (intra) group edges. Instead, a blockmatrix is passed to the function with the desired block structure. The example below illustrates the technique with a network composed of three groups with differing inter/intra group edge patterns.
```{r general_example}
# create a signed network with three groups and different inter/intra group ties
g1 <- g2 <- g3 <- graph.full(5)

V(g1)$name <- as.character(1:5)
V(g2)$name <- as.character(6:10)
V(g3)$name <- as.character(11:15)

g <- Reduce("%u%", list(g1, g2, g3))
E(g)$sign <- 1
E(g)$sign[1:10] <- -1
g <- add.edges(g, c(rbind(1:5, 6:10)), attr = list(sign = -1))
g <- add.edges(g, c(rbind(1:5, 11:15)), attr = list(sign = -1))
g <- add.edges(g, c(rbind(11:15, 6:10)), attr = list(sign = 1))

# specify the link patterns between groups
blockmat <- matrix(c(1, -1, -1, -1, 1, 1, -1, 1, -1), 3, 3, byrow = TRUE)
blockmat

clu <- signed_blockmodel_general(g, blockmat, 0.5)
clu
ggblock(g, clu$membership, show_blocks = TRUE, show_labels = FALSE)
```

# How to reach out? 

### Where do I report bugs?

Simply [open an issue](https://github.com/schochastics/signnet/issues/new) on GitHub.

### How do I contribute to the package?

If you have an idea (but no code yet), [open an issue](https://github.com/schochastics/signnet/issues/new) on GitHub. If you want to contribute with a specific feature and have the code ready, fork the repository, add your code, and create a pull request.

### Do you need support?

The easiest way is to [open an issue](https://github.com/schochastics/signnet/issues/new) - this way, your question is also visible to others who may face similar problems.

### Code of Conduct

Please note that the signnet project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

Owner

Name: David Schoch
Login: schochastics
Kind: user
Location: Germany
Company: cynkra

Website: mr.schochastics.net
Repositories: 131
Profile: https://github.com/schochastics

Data Scientist/DevOps Engineer at cynkra and #RStats developer

JOSS Publication

signnet: An R package for analyzing signed networks

Published

January 27, 2023

DOI

10.21105/joss.04987

Volume 8, Issue 81, Page 4987

Authors

David Schoch

GESIS - Leibniz Institute for the Social Sciences

Editor

Sebastian Benthall

Citation (CITATION.cff)

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  blockmodeling algorithms from Doreian (2008) <https://doi.org/10.1016/j.socnet.2008.03.005>,
  various centrality indices, and projections of signed two-mode networks introduced
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cran.r-project.org: signnet

Methods to Analyse Signed Networks

Homepage: https://github.com/schochastics/signnet
Documentation: http://cran.r-project.org/web/packages/signnet/signnet.pdf
License: MIT + file LICENSE
Latest release: 1.0.5
published 11 months ago

Versions: 15
Dependent Packages: 0
Dependent Repositories: 0
Downloads: 423 Last month

Rankings

Forks count: 10.1%

Stargazers count: 11.9%

Average: 23.8%

Dependent packages count: 29.8%

Downloads: 31.7%

Dependent repos count: 35.5%

Maintainers (1)

david@schochastics.net

Last synced: 4 months ago

Dependencies

DESCRIPTION cran

R >= 3.2.0 depends
Matrix * imports
Rcpp * imports
igraph * imports
ROI * suggests
ROI.plugin.glpk * suggests
covr * suggests
ggplot2 * suggests
ggraph * suggests
knitr * suggests
ompr * suggests
ompr.roi * suggests
rmarkdown * suggests
testthat >= 2.1.0 suggests

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signnet

Science Score: 98.0%

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signnet: An R package for analyzing signed networks

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