Title:	A Collection of Tools for Network Analysis
Version:	0.8.3
Description:	Provides a collection of network analytic (convenience) functions which are missing in other standard packages. This includes triad census with attributes <doi:10.1016/j.socnet.2019.04.003>, core-periphery models <doi:10.1016/S0378-8733(99)00019-2>, and several graph generators. Most functions are build upon 'igraph'.
URL:	https://github.com/schochastics/netUtils/, https://schochastics.github.io/netUtils/
BugReports:	https://github.com/schochastics/netUtils/issues
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
LinkingTo:	Rcpp, RcppArmadillo
Imports:	Rcpp, igraph (≥ 2.0.0), stats
Suggests:	covr, GA, testthat (≥ 3.0.0)
Config/testthat/edition:	3
NeedsCompilation:	yes
Packaged:	2024-10-10 19:11:30 UTC; david
Author:	David Schoch [aut, cre]
Maintainer:	David Schoch <david@schochastics.net>
Repository:	CRAN
Date/Publication:	2024-10-10 19:30:02 UTC

Adjacency list

Description

Create adjacency lists from a graph, either for adjacent edges or for neighboring vertices. This version is faster than the version of igraph but less general.

Usage

as_adj_list1(g)

Arguments

Details

The function does not have a mode parameter and only returns the adjacency list comparable to as_adj_list(g,mode="all)

Value

A list of numeric vectors.

Author(s)

David Schoch

Examples

library(igraph)
g <- make_ring(10)
as_adj_list1(g)

weighted dense adjacency matrix

Description

returns the weighted adjacency matrix in dense format

Usage

as_adj_weighted(g, attr = NULL)

Arguments

Details

This method is faster than as_adj from igraph if you need the weighted adjacency matrix in dense format

Value

Numeric matrix

Author(s)

David Schoch

Examples

library(igraph)
g <- sample_gnp(10, 0.2)
E(g)$weight <- runif(ecount(g))
as_adj_weighted(g, attr = "weight")

Convert a list of graphs to an adjacency matrices

Description

Convenience function that turns a list of igraph objects into adjacency matrices.

Usage

as_multi_adj(g_lst, attr = NULL, sparse = FALSE)

Arguments

Value

List of numeric matrices

Author(s)

David Schoch

two-mode network from a data.frame

Description

Create a two-mode network from a data.frame

Usage

bipartite_from_data_frame(d, type1, type2, attr = NULL, weighted = TRUE)

Arguments

Value

two mode network as igraph object

Author(s)

David Schoch

Examples

library(igraph)
edges <- data.frame(mode1 = 1:5, mode2 = letters[1:5])
bipartite_from_data_frame(edges, "mode1", "mode2")

Clique Vertex Matrix

Description

Creates the clique vertex matrix with entries (i,j) equal to one if node j is in clique i

Usage

clique_vertex_mat(g)

Arguments

Value

Numeric matrix

Author(s)

David Schoch

Examples

library(igraph)
g <- sample_gnp(10, 0.2)
clique_vertex_mat(g)

Discrete core-periphery model

Description

Fits a discrete core-periphery model to a given network

Usage

core_periphery(graph, method = "rk1_dc", iter = 500, ...)

Arguments

Details

The function fits the data to an optimal pattern matrix with a genetic algorithm (method="GA") or a rank 1 approximation, either with degree centrality (method="rk1_dc") or eigenvector centrality (method="rk1_ec") . The rank 1 approximation is computationally far cheaper but also more experimental. Best is to compare the results from both models.

Value

list with numeric vector with entries (k1,k2,...ki...) where ki assigns vertex i to either the core (ki=1) or periphery (ki=0), and the maximal correlation with an optimal pattern matrix

Author(s)

David Schoch

References

Borgatti, Stephen P., and Martin G. Everett. "Models of core/periphery structures." Social networks 21.4 (2000): 375-395.

Examples

set.seed(121)
# split graphs have a perfect core-periphery structure
sg <- split_graph(n = 20, p = 0.3, core = 0.5)
core_periphery(sg)

dyad census with node attributes

Description

dyad census with node attributes

Usage

dyad_census_attr(g, vattr)

Arguments

Details

The node attribute should be integers from 1 to max(attr)

Value

dyad census as a data.frame.

Author(s)

David Schoch

Examples

library(igraph)
g <- sample_gnp(10, 0.4, directed = TRUE)
V(g)$attr <- c(rep(1, 5), rep(2, 5))
dyad_census_attr(g, "attr")

Find Cliques, maximal or not, fast

Description

Enumerates all (maximal) cliques using MACE. Can be faster than igraph in some circumstances

Usage

fast_cliques(g, what = "M", min = NULL, max = NULL, outfile = NA)

Arguments

Details

C Code downloaded from http://research.nii.ac.jp/~uno/codes.htm. Download the code and run make and then point an environment variable called MACE_PATH to the binary. See http://research.nii.ac.jp/~uno/code/mace.html for more details. MACE is faster than igraph for dense graphs.

Value

a list containing numeric vectors of vertex ids. Each list element is a clique. If outfile!=NA, the output is written to the specified file

Author(s)

David Schoch

References

Kazuhisa Makino, Takeaki Uno, "New Algorithms for Enumerating All Maximal Cliques", Lecture Notes in Computer Science 3111 (Proceedings of SWAT 2004), Springer, pp.260-272, 2004

Cartesian product of two graphs

Description

Compute the Cartesian product of two graphs

Usage

graph_cartesian(g, h)

Arguments

Details

See https://en.wikipedia.org/wiki/Cartesian_product_of_graphs

Value

Cartesian product as igraph object

Author(s)

David Schoch

Examples

library(igraph)
g <- make_ring(4)
h <- make_full_graph(2)
graph_cartesian(g, h)

Graph correlation

Description

This function computes the correlation between networks. Implemented methods expect the graph to be an adjacency matrix, an igraph, or a network object.

Usage

graph_cor(object1, object2)

## Default S3 method:
graph_cor(object1, object2)

## S3 method for class 'igraph'
graph_cor(object1, object2, ...)

## S3 method for class 'matrix'
graph_cor(object1, object2)

## S3 method for class 'array'
graph_cor(object1, object2)

Arguments

Value

correlation between graphs

Direct product of two graphs

Description

Compute the direct product of two graphs

Usage

graph_direct(g, h)

Arguments

Details

See https://en.wikipedia.org/wiki/Tensor_product_of_graphs

Value

Direct product as igraph object

Author(s)

David Schoch

Examples

library(igraph)
g <- make_ring(4)
h <- make_full_graph(2)
graph_direct(g, h)

Multiple networks from a single edgelist with a typed attribute

Description

Create a list of igraph objects from an edgelist according to a type attribute

Usage

graph_from_multi_edgelist(
  d,
  from = NULL,
  to = NULL,
  type = NULL,
  weight = NULL,
  directed = FALSE
)

Arguments

Value

list of igraph objects.

Author(s)

David Schoch

Examples

library(igraph)
d <- data.frame(
    from = rep(c(1, 2, 3), 3), to = rep(c(2, 3, 1), 3),
    type = rep(c("a", "b", "c"), each = 3), weight = 1:9
)
graph_from_multi_edgelist(d, "from", "to", "type", "weight")

k partite graphs

Description

Create a random k-partite graph.

Usage

graph_kpartite(n = 10, grp = c(5, 5))

Arguments

Value

igraph object

Author(s)

David Schoch

Examples

# 3-partite graph with equal sized groups
graph_kpartite(n = 15, grp = c(5, 5, 5))

helper function

Description

small functions to deal with typical network problems

Usage

biggest_component(g)

delete_isolates(g)

Arguments

Value

igraph object

Author(s)

David Schoch

Reciprocity correlation coefficient

Description

Reciprocity correlation coefficient

Usage

reciprocity_cor(g)

Arguments

Details

The usual definition of reciprocity has some defects. It cannot tell the relative difference of reciprocity compared with purely random network with the same number of vertices and edges. The useful information from reciprocity is not the value itself, but whether mutual links occur more or less often than expected by chance.

To overcome this issue, reciprocity can be defined as the correlation coefficient between the entries of the adjacency matrix of a directed graph:

\frac{\sum_{i\neq j} (a_{ij} - a')((a_{ji} - a')}{\sum_{i\neq j} (a_{ij} - a')^2}

where a' is the density of g.

This definition gives an absolute quantity which directly allows one to distinguish between reciprocal (>0) and antireciprocal (< 0) networks, with mutual links occurring more and less often than random respectively.

Value

Reciprocity as a correlation

Author(s)

David Schoch

References

Diego Garlaschelli; Loffredo, Maria I. (2004). "Patterns of Link Reciprocity in Directed Networks". Physical Review Letters. American Physical Society. 93 (26): 268701

Examples

library(igraph)
g <- sample_gnp(20, p = 0.3, directed = TRUE)
reciprocity(g)
reciprocity_cor(g)

Generate random graphs with a given coreness sequence

Description

Similar to sample_degseq just with coreness

Usage

sample_coreseq(cores)

Arguments

Details

The code is an adaption of the python code from https://github.com/ktvank/Random-Graphs-with-Prescribed-K-Core-Sequences/

Value

igraph object of graph with the same coreness sequence as the input

Author(s)

David Schoch

References

Van Koevering, Katherine, Austin R. Benson, and Jon Kleinberg. 2021. ‘Random Graphs with Prescribed K-Core Sequences: A New Null Model for Network Analysis’. ArXiv:2102.12604. https://doi.org/10.1145/3442381.3450001.

Examples

library(igraph)
g1 <- make_graph("Zachary")
kcores1 <- coreness(g1)
g2 <- sample_coreseq(kcores1)
kcores2 <- coreness(g2)

# the sorted arrays are the same
all(sort(kcores1) == sort(kcores2))

LFR benchmark graphs

Description

Generates benchmark networks for clustering tasks with a priori known communities. The algorithm accounts for the heterogeneity in the distributions of node degrees and of community sizes.

Usage

sample_lfr(
  n,
  tau1 = 2,
  tau2 = 1,
  mu = 0.1,
  average_degree,
  max_degree,
  min_community = NULL,
  max_community = NULL,
  on = 0,
  om = 0
)

Arguments

Details

code adapted from https://github.com/synwalk/synwalk-analysis/tree/master/lfr_generator

Value

an igraph object

References

A. Lancichinetti, S. Fortunato, and F. Radicchi.(2008) Benchmark graphs for testing community detection algorithms. Physical Review E, 78. arXiv:0805.4770

Examples

# Simple Girven-Newman benchmark graphs
g <- sample_lfr(
    n = 128, average_degree = 16,
    max_degree = 16, mu = 0.1,
    min_community = 32, max_community = 32
)

Homophilic random graph using BA preferential attachment model

Description

A graph of n nodes is grown by attaching new nodes each with m edges that are preferentially attached to existing nodes with high degree, depending on the homophily parameters.

Usage

sample_pa_homophilic(
  n,
  m,
  minority_fraction,
  h_ab,
  h_ba = NULL,
  directed = FALSE
)

Arguments

Details

The code is an adaption of the python code from https://github.com/gesiscss/HomophilicNtwMinorities/

Value

igraph object

Author(s)

David Schoch #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)

References

Karimi, F., Génois, M., Wagner, C., Singer, P., & Strohmaier, M. (2018). Homophily influences ranking of minorities in social networks. Scientific reports, 8(1), 1-12. (https://www.nature.com/articles/s41598-018-29405-7)

Espín-Noboa, L., Wagner, C., Strohmaier, M., & Karimi, F. (2022). Inequality and inequity in network-based ranking and recommendation algorithms. Scientific reports, 12(1), 1-14. (https://www.nature.com/articles/s41598-022-05434-1)

split graph

Description

Create a random split graph with a perfect core-periphery structure.

Usage

split_graph(n, p, core)

Arguments

Value

igraph object

Author(s)

David Schoch

Examples

# split graph with 20 nodes and a core size of 10
split_graph(n = 20, p = 0.4, 0.5)

Print graphs to terminal

Description

Prints an igraph object to terminal (different than the standard igraph method)

Usage

## S3 method for class 'igraph'
str(object, ...)

Arguments

Value

str does not return anything. The obvious side effect is output to the terminal.

Author(s)

David Schoch

Maximal Structural Equivalence

Description

Calculates structural equivalence for an undirected graph

Usage

structural_equivalence(g)

Arguments

Details

Two nodes u and v are structurally equivalent if they have exactly the same neighbors. The equivalence classes produced with this function are either cliques or empty graphs.

Value

vector of equivalence classes

Author(s)

David Schoch

triad census with node attributes

Description

triad census with node attributes

Usage

triad_census_attr(g, vattr)

Arguments

Details

The node attribute should be integers from 1 to max(attr). 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 implemented algorithm is comparable to the algorithm in Lienert et al.

Value

triad census with node attributes

Author(s)

David Schoch

References

Lienert, J., Koehly, L., Reed-Tsochas, F., & Marcum, C. S. (2019). An efficient counting method for the colored triad census. Social Networks, 58, 136-142.

Examples

library(igraph)
set.seed(112)
g <- sample_gnp(20, p = 0.3, directed = TRUE)
# add a vertex attribute
V(g)$type <- rep(1:2, each = 10)
triad_census_attr(g, "type")