# Extending network analysis in R with netUtils

R
package
networks
Author

David Schoch

Published

August 27, 2022

This post was semi automatically converted from blogdown to Quarto and may contain errors. The original can be found in the archive.

During the last 5 years, I have accumulated various scripts with (personal) convenience functions for network analysis and I also implemented new methods from time to time which I could not find in any other package in R. The package `netUtils` gathers all these functions and makes them available for anyone who may also needs to apply “non-standard” network analytic tools. In this post, I will briefly highlight some of the most prominent functions of the package. All available functions are listed in the README on github.

``````# developer version
remotes::install_github("schochastics/netUtils")

install.packages("netUtils")``````

# Random graph generators

The package includes three new random graph generators:

• `graph_kpartite()` creates a random k-partite network.
• `sample_coreseq()` creates a random graph with given coreness sequence.
• `sample_pa_homophilic()` creates a preferential attachment graph with two groups of nodes.
• `split_graph()` sample graph with perfect core-periphery structure

`graph_kpartite()` can be used to construct a complete k-partite graph. A k-partite graph is a graph with k groups of nodes where no two nodes within the same group are connected but are connected to all other nodes in other groups.

The example below shows a 3-partite graph where each group consists of 5 nodes.

``g <- graph_kpartite(n = 15, grp = c(5,5,5))``

The function `sample_coreseq()` is conceptually very similar to the function `sample_degseq()` in {{igraph}}. Instead of sampling networks with the same degree sequence, `sample_coreseq()` samples network which have the same k-core decomposition

``````g1 <- sample_gnp(40,0.1)
kcore1 <- sort(coreness(g1))
g2 <- sample_coreseq(kcore1)
kcore2 <- sort(coreness(g2))
all(kcore1==kcore2)``````
``## [1] TRUE``

`sample_pa_homophilic()` creates a preferential attachment graph with two groups of nodes. The parameter `h_ab` is used to adjust the probability that an edge between groups occurs. A network is maximally heterophilic if `h_ab=1`, that is there only exist edges between groups, and maximally homophilic if `h_ab=0`, that is there only exist edges within groups.

``````# maximally heterophilic network
sample_pa_homophilic(n = 50, m = 2,minority_fraction = 0.2,h_ab = 1)
# maximally homophilic network
sample_pa_homophilic(n = 50, m = 2,minority_fraction = 0.2,h_ab = 0)``````

The figure below shows some examples for varying degrees of homophily.

The function `split_graph()` can be used to create graphs with a perfect core-periphery structure. This means that there are two groups of nodes: One forms a clique (the core: all nodes are pairwise connected) and the other group is only connected to nodes in the core (the periphery: all nodes are pairise disconnected)

In the below example, we create a split graph with 100 nodes and core size 2o (100*0.2)

``sg <- split_graph(n = 100,p = 0.3,core = 0.2)``

The figure below shows the typical pattern of the adjacency matrix of a split graph.

# Analytic functions

The most important analytic functions are

• `triad_census_attr()` which calculates the triad census with vertex attributes.
• `core_periphery()` which fits a discrete core periphery model.
``````set.seed(112)
g <- sample_gnp(20,p = 0.3,directed = TRUE)
# add a vertex attribute
V(g)\$type <- rep(1:2,each = 10)
``````##  T003-111  T003-112  T003-122  T003-222  T012-111  T012-121  T012-112  T012-122
##         8        33        28         7        32        40        31        19
##  T012-211  T012-221  T012-212  T012-222 T021D-111 T021D-211 T021D-112 T021D-212
##        27        41        25        26         9        19        19        21
## T021D-122 T021D-222  T102-111  T102-112  T102-122  T102-211  T102-212  T102-222
##         7        10        11        18        16         5        19        10
## T021C-111 T021C-211 T021C-121 T021C-221 T021C-112 T021C-212 T021C-122 T021C-222
##        17        23        29        17        19         7        24        10
## T111U-111 T111U-121 T111U-112 T111U-122 T111U-211 T111U-221 T111U-212 T111U-222
##         9        16         7        21         5        13        10         6
## T021U-111 T021U-112 T021U-122 T021U-211 T021U-212 T021U-222 T030T-111 T030T-121
##        11        19        13         3        14         7        11        11
## T030T-112 T030T-122 T030T-211 T030T-221 T030T-212 T030T-222 T120U-111 T120U-112
##        11        13        10        14         8         5         1         8
## T120U-122 T120U-211 T120U-212 T120U-222 T111D-111 T111D-121 T111D-112 T111D-122
##         6         0         4         4         4        12         8        13
## T111D-211 T111D-221 T111D-212 T111D-222  T201-111  T201-112  T201-121  T201-122
##        14        20        10        15         0         5         3         5
##  T201-221  T201-222 T030C-111 T030C-112 T030C-122 T030C-222 T120C-111 T120C-121
##         3         3         2        12        14         3         3         8
## T120C-211 T120C-221 T120C-112 T120C-122 T120C-212 T120C-222 T120D-111 T120D-112
##         7         5         5         7         7         6         0         9
## T120D-211 T120D-212 T120D-122 T120D-222  T210-111  T210-121  T210-211  T210-221
##         1         9         4         1         2         8         3         5
##  T210-112  T210-122  T210-212  T210-222  T300-111  T300-112  T300-122  T300-222
##         1         3         5         5         0         1         0         2``````

The output is a named vector where the names are of the form Txxx-abc, where xxx corresponds to the standard triad census notation and “abc” are the attributes of the involved nodes.

The function `core_periphery()` fits a standard discrete core-periphery model to the data

``````#graph with perfect core-periphery structure
core_graph <- split_graph(n = 100, p = 0.3, core = 0.2)
core_periphery(core_graph)``````
``````## \$vec
##   [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##  [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
##
## \$corr
## [1] 1``````
``````# random graphs have a very weak core-periphery structure
rgraph <- sample_gnp(n = 100,p = 0.2)
core_periphery(rgraph)``````
``````## \$vec
##   [1] 1 0 1 0 1 0 0 0 1 0 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1 1 0 0 1 0 1 0 1 0 0 0 0
##  [38] 1 0 1 0 1 0 1 1 1 1 1 1 0 0 1 0 0 1 0 1 0 0 0 1 1 1 1 0 1 0 1 0 1 0 0 1 0
##  [75] 1 0 1 0 0 1 1 1 0 0 1 1 0 0 1 1 0 0 1 0 0 0 0 1 0 0
##
## \$corr
## [1] 0.162141``````

More advanced core-periphery models are planned for a future release.

# A new print method

I also extended `str` to work with igraph objects for an alternative way of printing igraph objects using additional information.

``````library(networkdata)
data("greys")
str(greys)``````
``````## -----------------------------------------------------------
## UNNAMED NETWORK (undirected, unweighted, one-mode network)
## -----------------------------------------------------------
## Nodes: 54, Edges: 57, Density: 0.0398, Components: 4, Isolates: 0
## -Vertex Attributes:
##  name(c): Addison Montgomery, Adele Webber, Teddy Altman, Amelia ...
##  sex(c): F, F, F, F, F, F, M, F, M, M, F, M, M, M, F, M, F, F, M, F, M, ...
##  race(c): White, Black, White, White, White, White, Black, Black, Black, ...
##  birthyear(n): 1967, 1949, 1969, 1981, 1976, 1975, 1981, 1969, 1972, ...
##  position(c): Attending, Non-Staff, Attending, Attending, Attending, ...
##  season(n): 1, 2, 6, 7, 5, 3, 6, 1, 6, 7, 8, 3, 2, 1, 1, 2, 1, 2, 1, 1, ...
##  sign(c): Libra, Leo, Pisces, Libra, Leo, Gemini, Leo, Virgo, Aquarius, ...
## ---
## -Edges (first 10):
##  Arizona Robbins->Leah Murphy Alex Karev->Leah Murphy Arizona
## Robbins->Lauren Boswell Arizona Robbins->Callie Torres Erica
## Hahn->Callie Torres Alex Karev->Callie Torres Mark Sloan->Callie Torres
## George O'Malley->Callie Torres Izzie Stevens->George O'Malley Meredith
## Grey->George O'Malley``````

# schochastics

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## Citation

BibTeX citation:
``````@online{schoch2022,
author = {Schoch, David},
title = {Extending Network Analysis in {R} with {netUtils}},
date = {2022-08-27},
url = {http://blog.schochastics.net/posts/2022-08-27_extending-network-analysis-in-r-with-netutils},
langid = {en}
}
``````
For attribution, please cite this work as:
Schoch, David. 2022. “Extending Network Analysis in R with netUtils.” August 27, 2022. http://blog.schochastics.net/posts/2022-08-27_extending-network-analysis-in-r-with-netutils.