Title:	Additional Layout Algorithms for Network Visualizations
Version:	1.2.2
Description:	Several new layout algorithms to visualize networks are provided which are not part of 'igraph'. Most are based on the concept of stress majorization by Gansner et al. (2004) <doi:10.1007/978-3-540-31843-9_25>. Some more specific algorithms allow the user to emphasize hidden group structures in networks or focus on specific nodes.
URL:	https://github.com/schochastics/graphlayouts, https://schochastics.github.io/graphlayouts/
BugReports:	https://github.com/schochastics/graphlayouts/issues
Depends:	R (≥ 3.6.0)
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
Imports:	igraph (≥ 2.0.0), Rcpp
Suggests:	testthat, ggplot2, uwot, oaqc
LinkingTo:	Rcpp, RcppArmadillo
RoxygenNote:	7.3.2
NeedsCompilation:	yes
Packaged:	2025-01-23 13:37:56 UTC; david
Author:	David Schoch [aut, cre]
Maintainer:	David Schoch <david@schochastics.net>
Repository:	CRAN
Date/Publication:	2025-01-23 16:20:06 UTC

graphlayouts: layout algorithms for network visualizations

Description

The package implements several new layout algorithms to visualize networks. Most are based on the concept of stress majorization. Some more specific algorithms allow to emphasize hidden group structures in networks or focus on specific nodes. The package is best used in conjunction with ggraph.

Some features of the package are:

• layout_with_stress() is a state of the art deterministic layout algorithms.

• layout_as_backbone() uncovers hidden group structures (if they exist) by emphasizing strongly embedded edges.

• layout_with_focus() and layout_with_centrality() produce concentric layouts with a focal or most central nodes in the center.

• layout_with_eigen() implements some layout algorithms on the basis of eigenvectors

• layout_with_sparse_stress() sparse stress for large graphs

• layout_with_pmds() pivot MDS for large graphs.

• layout_as_dynamic() for longitudinal network data

A detailed tutorial can be found at https://schochastics.github.io/netVizR/

Author(s)

Maintainer: David Schoch david@schochastics.net (ORCID)

See Also

Useful links:

• https://github.com/schochastics/graphlayouts

• https://schochastics.github.io/graphlayouts/

• Report bugs at https://github.com/schochastics/graphlayouts/issues

annotate concentric circles

Description

annotate concentric circles

Usage

annotate_circle(cent, col = "#00BFFF", format = "", pos = "top", text_size = 3)

Arguments

Details

this function is best used with layout_with_centrality together with draw_circle.

Value

annotated concentric circles around origin

Examples

library(igraph)

g <- sample_gnp(10, 0.4)
## Not run: 
library(ggraph)
ggraph(g, layout = "centrality", centrality = closeness(g)) +
    draw_circle(use = "cent") +
    annotate_circle(closeness(g), pos = "bottom", format = "scientific") +
    geom_edge_link() +
    geom_node_point(shape = 21, fill = "grey25", size = 5) +
    theme_graph() +
    coord_fixed()

## End(Not run)

Draw concentric circles

Description

Draw concentric circles

Usage

draw_circle(col = "#00BFFF", use = "focus", max.circle)

Arguments

Details

this function is best used with a concentric layout such as layout_with_focus and layout_with_centrality.

Value

concentric circles around origin

Examples

library(igraph)
g <- sample_gnp(10, 0.4)

## Not run: 
library(ggraph)
ggraph(g, layout = "centrality", centrality = degree(g)) +
    draw_circle(use = "cent") +
    geom_edge_link() +
    geom_node_point(shape = 21, fill = "grey25", size = 5) +
    theme_graph() +
    coord_fixed()

## End(Not run)

Manipulate graph

Description

functions to manipulate a graph

Usage

reorder_edges(g, attr, desc = TRUE)

Arguments

Details

reorder_edges() allows to reorder edges according to an attribute so that edges are drawn in the given order.

Value

manipulated graph

Author(s)

David Schoch

Examples

library(igraph)

g <- sample_gnp(10, 0.5)
E(g)$attr <- 1:ecount(g)
gn <- reorder_edges(g,"attr")

Metro Map Layout

Description

Metro map layout based on multicriteria optimization

Usage

layout_as_metromap(object, xy, l = 2, gr = 0.0025, w = rep(1, 5), bsize = 5)

Arguments

Details

The function optimizes the following five criteria using a hill climbing algorithm:

• Angular Resolution Criterion: The angles of incident edges at each station should be maximized, because if there is only a small angle between any two adjacent edges, then it can become difficult to distinguish between them

• Edge Length Criterion: The edge lengths across the whole map should be approximately equal to ensure regular spacing between stations. It is based on the preferred multiple, l, of the grid spacing, g. The purpose of the criterion is to penalize edges that are longer than or shorter than lg.

• Balanced Edge Length Criterion: The length of edges incident to a particular station should be similar

• Line Straightness Criterion: (not yet implemented) Edges that form part of a line should, where possible, be co-linear either side of each station that the line passes through

• Octilinearity Criterion: Each edge should be drawn horizontally, vertically, or diagonally at 45 degree, so we penalize edges that are not at a desired angle

Value

new coordinates for stations

Author(s)

David Schoch

References

Stott, Jonathan, et al. "Automatic metro map layout using multicriteria optimization." IEEE Transactions on Visualization and Computer Graphics 17.1 (2010): 101-114.

Examples

# the algorithm has problems with parallel edges
library(igraph)
g <- simplify(metro_berlin)
xy <- cbind(V(g)$lon, V(g)$lat) * 100

# the algorithm is not very stable. try playing with the parameters
## Not run: 
xy_new <- layout_as_metromap(g, xy, l = 2, gr = 0.5, w = c(100, 100, 1, 1, 100), bsize = 35)

## End(Not run)

backbone graph layout

Description

emphasizes a hidden group structure if it exists in the graph. Calculates a layout for a sparsified network only including the most embedded edges. Deleted edges are added back after the layout is calculated.

Usage

layout_as_backbone(g, keep = 0.2, backbone = TRUE)

layout_igraph_backbone(g, keep = 0.2, backbone = TRUE, circular)

Arguments

Details

The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'. 'ggraph' natively supports the layout.

Value

list of xy coordinates and vector of edge ids included in the backbone

References

Nocaj, A., Ortmann, M., & Brandes, U. (2015). Untangling the hairballs of multi-centered, small-world online social media networks. Journal of Graph Algorithms and Applications: JGAA, 19(2), 595-618.

Examples

library(igraph)
## Not run: 
g <- sample_islands(9, 20, 0.4, 9)
g <- simplify(g)
V(g)$grp <- as.character(rep(1:9, each = 20))
bb <- layout_as_backbone(g, keep = 0.4)

# add backbone links as edge attribute
E(g)$col <- FALSE
E(g)$col[bb$backbone] <- TRUE

## End(Not run)

radial centrality layout

Description

arranges nodes in concentric circles according to a centrality index.

Usage

layout_with_centrality(
  g,
  cent,
  scale = TRUE,
  iter = 500,
  tol = 1e-04,
  tseq = seq(0, 1, 0.2)
)

layout_igraph_centrality(
  g,
  cent,
  scale = TRUE,
  iter = 500,
  tol = 1e-04,
  tseq = seq(0, 1, 0.2),
  circular
)

Arguments

Details

The function optimizes a convex combination of regular stress and a constrained stress function which forces nodes to be arranged on concentric circles. The vector tseq is the sequence of parameters used for the convex combination. In iteration i of the algorithm tseq[i] is used to combine regular and constraint stress as (1-tseq[i])*stress_{regular}+tseq[i]*stress_{constraint}. The sequence must be increasing, start at zero and end at one. The default setting should be a good choice for most graphs.

The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'. 'ggraph' natively supports the layout.

Value

matrix of xy coordinates

References

Brandes, U., & Pich, C. (2011). More flexible radial layout. Journal of Graph Algorithms and Applications, 15(1), 157-173.

See Also

layout_centrality_group

Examples

library(igraph)

g <- sample_gnp(10, 0.4)
## Not run: 
library(ggraph)
ggraph(g, layout = "centrality", centrality = closeness(g)) +
    draw_circle(use = "cent") +
    geom_edge_link0() +
    geom_node_point(shape = 21, fill = "grey25", size = 5) +
    theme_graph() +
    coord_fixed()

## End(Not run)

radial centrality group layout

Description

arranges nodes in concentric circles according to a centrality index and keeping groups within a angle range

Usage

layout_with_centrality_group(g, cent, group, shrink = 10, ...)

layout_igraph_centrality_group(g, cent, group, shrink = 10, circular, ...)

Arguments

Value

matrix of xy coordinates

See Also

layout_centrality

Examples

library(igraph)

constrained stress layout

Description

force-directed graph layout based on stress majorization with variable constrained

Usage

layout_with_constrained_stress(
  g,
  coord,
  fixdim = "x",
  weights = NA,
  iter = 500,
  tol = 1e-04,
  mds = TRUE,
  bbox = 30
)

layout_igraph_constrained_stress(
  g,
  coord,
  fixdim = "x",
  weights = NA,
  iter = 500,
  tol = 1e-04,
  mds = TRUE,
  bbox = 30,
  circular
)

Arguments

Details

Be careful when using weights. In most cases, the inverse of the edge weights should be used to ensure that the endpoints of an edges with higher weights are closer together (weights=1/E(g)$weight).

The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'. 'ggraph' natively supports the layout.

Value

matrix of xy coordinates

References

Gansner, E. R., Koren, Y., & North, S. (2004). Graph drawing by stress majorization. In International Symposium on Graph Drawing (pp. 239-250). Springer, Berlin, Heidelberg.

See Also

layout_constrained_stress3D

constrained stress layout in 3D

Description

force-directed graph layout based on stress majorization with variable constrained in 3D

Usage

layout_with_constrained_stress3D(
  g,
  coord,
  fixdim = "x",
  weights = NA,
  iter = 500,
  tol = 1e-04,
  mds = TRUE,
  bbox = 30
)

Arguments

Details

Be careful when using weights. In most cases, the inverse of the edge weights should be used to ensure that the endpoints of an edges with higher weights are closer together (weights=1/E(g)$weight).

This function does not come with direct support for igraph or ggraph.

Value

matrix of xyz coordinates

References

Gansner, E. R., Koren, Y., & North, S. (2004). Graph drawing by stress majorization. In International Symposium on Graph Drawing (pp. 239-250). Springer, Berlin, Heidelberg.

See Also

layout_constrained_stress

dynamic graph layout

Description

Create layouts for longitudinal networks.

Usage

layout_as_dynamic(gList, weights = NA, alpha = 0.5, iter = 500, tol = 1e-04)

Arguments

Details

The reference layout is calculated based on the union of all graphs. The parameter alpha controls the influence of the reference layout. For alpha=1, only the reference layout is used and all graphs have the same layout. For alpha=0, the stress layout of each individual graph is used. Values in-between interpolate between the two layouts.

Be careful when using weights. In most cases, the inverse of the edge weights should be used to ensure that the endpoints of an edges with higher weights are closer together (weights=1/E(g)$weight).

Value

list of coordinates for each graph

References

Brandes, U. and Indlekofer, N. and Mader, M. (2012). Visualization methods for longitudinal social networks and stochastic actor-oriented modeling. Social Networks 34 (3) 291-308

Examples

library(igraph)
g1 <- sample_gnp(20, 0.2)
g2 <- sample_gnp(20, 0.2)
g3 <- sample_gnp(20, 0.2)

xy <- layout_as_dynamic(list(g1, g2, g3))

# layout for first network
xy[[1]]

Layout with fixed coordinates

Description

force-directed graph layout based on stress majorization with fixed coordinates for some nodes

Usage

layout_with_fixed_coords(
  g,
  coords,
  weights = NA,
  iter = 500,
  tol = 1e-04,
  mds = TRUE,
  bbox = 30
)

layout_igraph_fixed_coords(
  g,
  coords,
  weights = NA,
  iter = 500,
  tol = 1e-04,
  mds = TRUE,
  bbox = 30,
  circular
)

Arguments

Details

Be careful when using weights. In most cases, the inverse of the edge weights should be used to ensure that the endpoints of an edges with higher weights are closer together (weights=1/E(g)$weight).

The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'. 'ggraph' natively supports the layout.

Value

matrix of xy coordinates

See Also

layout_constrained_stress

Examples

library(igraph)
set.seed(12)
g <- sample_bipartite(10, 5, "gnp", 0.5)
fxy <- cbind(c(rep(0, 10), rep(1, 5)), NA)
xy <- layout_with_fixed_coords(g, fxy)

radial focus layout

Description

arrange nodes in concentric circles around a focal node according to their distance from the focus.

Usage

layout_with_focus(g, v, weights = NA, iter = 500, tol = 1e-04)

layout_igraph_focus(g, v, weights = NA, iter = 500, tol = 1e-04, circular)

Arguments

Details

Be careful when using weights. In most cases, the inverse of the edge weights should be used to ensure that the endpoints of an edges with higher weights are closer together (weights=1/E(g)$weight).

Value

a list containing xy coordinates and the distances to the focal node

References

Brandes, U., & Pich, C. (2011). More flexible radial layout. Journal of Graph Algorithms and Applications, 15(1), 157-173.

See Also

layout_focus_group The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'. 'ggraph' natively supports the layout.

Examples

library(igraph)
g <- sample_gnp(10, 0.4)
coords <- layout_with_focus(g, v = 1)
coords

radial focus group layout

Description

arrange nodes in concentric circles around a focal node according to their distance from the focus and keep predefined groups in the same angle range.

Usage

layout_with_focus_group(
  g,
  v,
  group,
  shrink = 10,
  weights = NA,
  iter = 500,
  tol = 1e-04
)

layout_igraph_focus_group(
  g,
  v,
  group,
  shrink = 10,
  weights = NA,
  iter = 500,
  tol = 1e-04,
  circular
)

Arguments

Details

Be careful when using weights. In most cases, the inverse of the edge weights should be used to ensure that the endpoints of an edges with higher weights are closer together (weights=1/E(g)$weight).

Value

matrix of xy coordinates

See Also

layout_focus The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'.

Examples

library(igraph)
g <- sample_islands(4, 5, 0.8, 2)
grp <- as.character(rep(1:4, each = 5))
layout_with_focus_group(g, v = 1, group = grp, shrink = 10)

manipulate layout

Description

functions to manipulate an existing layout

Usage

layout_rotate(xy, angle)

layout_mirror(xy, axis = "vertical")

Arguments

Details

These functions are mostly useful for deterministic layouts such as layout_with_stress

Value

manipulated matrix of xy coordinates

Author(s)

David Schoch

Examples

library(igraph)
g <- sample_gnp(50, 0.3)

xy <- layout_with_stress(g)

# rotate 90 degrees
xy <- layout_rotate(xy, 90)

# flip horizontally
xy <- layout_mirror(xy, "horizontal")

multilevel layout

Description

Layout algorithm to visualize multilevel networks

Usage

layout_as_multilevel(
  g,
  type = "all",
  FUN1,
  FUN2,
  params1 = NULL,
  params2 = NULL,
  ignore_iso = TRUE,
  project2D = TRUE,
  alpha = 35,
  beta = 45
)

layout_igraph_multilevel(
  g,
  type = "all",
  FUN1,
  FUN2,
  params1 = NULL,
  params2 = NULL,
  ignore_iso = TRUE,
  alpha = 35,
  beta = 45,
  circular
)

Arguments

Details

The algorithm internally computes a 3D layout where each level is in a separate y-plane. The layout is then projected into 2D via an isometric mapping, controlled by the parameters alpha and beta. It may take some adjusting to alpha and beta to find a good perspective.

If type="all", the layout is computed at once for the complete network. For type="separate", two user specified layout algorithms (FUN1 and FUN2) are used for the levels. The named lists param1 and param2 can be used to set parameters for FUN1 and FUN2. This option helpful for situations where different structural features of the levels should be emphasized.

For type="fix1" and type="fix2" only one of the level layouts is fixed. The other one is calculated by optimizing the inter level ties, such that they are drawn (almost) vertical.

The ignore_iso parameter controls the handling of isolates. If TRUE, nodes without inter level edges are ignored during the layout process and added at the end. If FALSE they are left unchanged

The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'.

Value

matrix of xy coordinates

Examples

library(igraph)
data("multilvl_ex")
## Not run: 
# compute a layout for the whole network
xy <- layout_as_multilevel(multilvl_ex, type = "all", alpha = 25, beta = 45)

# compute a layout for each level separately and combine them
xy <- layout_as_multilevel(multilvl_ex,
    type = "separate",
    FUN1 = layout_as_backbone,
    FUN2 = layout_with_stress,
    alpha = 25, beta = 45
)

## End(Not run)

pivot MDS graph layout

Description

similar to layout_with_mds but uses only a small set of pivots for MDS. Considerably faster than MDS and thus applicable for larger graphs.

Usage

layout_with_pmds(g, pivots, weights = NA, D = NULL, dim = 2)

layout_igraph_pmds(g, pivots, weights = NA, D = NULL, circular)

Arguments

Details

Be careful when using weights. In most cases, the inverse of the edge weights should be used to ensure that the endpoints of an edges with higher weights are closer together (weights=1/E(g)$weight)

The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'. 'ggraph' natively supports the layout.

Value

matrix of coordinates

Author(s)

David Schoch

References

Brandes, U. and Pich, C. (2006). Eigensolver Methods for Progressive Multidimensional Scaling of Large Data. In International Symposium on Graph Drawing (pp. 42-53). Springer

Examples

## Not run: 
library(igraph)
library(ggraph)

g <- sample_gnp(1000, 0.01)

xy <- layout_with_pmds(g, pivots = 100)

## End(Not run)

sparse stress graph layout

Description

stress majorization for larger graphs based on a set of pivot nodes.

Usage

layout_with_sparse_stress(g, pivots, weights = NA, iter = 500)

layout_igraph_sparse_stress(g, pivots, weights = NA, iter = 500, circular)

Arguments

Details

The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'. 'ggraph' natively supports the layout.

Value

matrix of xy coordinates

Author(s)

David Schoch

References

Ortmann, M. and Klimenta, M. and Brandes, U. (2016). A Sparse Stress Model. https://arxiv.org/pdf/1608.08909.pdf

Examples

## Not run: 
library(igraph)
library(ggraph)

g <- sample_gnp(1000, 0.005)

ggraph(g, layout = "sparse_stress", pivots = 100) +
    geom_edge_link0(edge_colour = "grey66") +
    geom_node_point(shape = 21, fill = "grey25", size = 5) +
    theme_graph()

## End(Not run)

spectral graph layouts

Description

Using a set of eigenvectors of matrices associated with a graph as coordinates

Usage

layout_with_eigen(g, type = "laplacian", ev = "smallest")

layout_igraph_eigen(g, type = "laplacian", ev = "smallest", circular)

Arguments

Details

The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'. 'ggraph' natively supports the layout.

Value

matrix of xy coordinates

Author(s)

David Schoch

Examples

library(igraph)

g <- sample_gnp(50, 0.2)

xy <- layout_with_eigen(g, type = "adjacency", ev = "largest")

xy <- layout_with_eigen(g, type = "adjacency", ev = "smallest")

xy <- layout_with_eigen(g, type = "laplacian", ev = "largest")

xy <- layout_with_eigen(g, type = "laplacian", ev = "smallest")

stress majorization layout

Description

force-directed graph layout based on stress majorization. Similar to Kamada-Kawai, but generally faster and with better results.

Usage

layout_with_stress(
  g,
  weights = NA,
  iter = 500,
  tol = 1e-04,
  mds = TRUE,
  bbox = 30
)

layout_igraph_stress(
  g,
  weights = NA,
  iter = 500,
  tol = 1e-04,
  mds = TRUE,
  bbox = 30,
  circular
)

Arguments

Details

Be careful when using weights. In most cases, the inverse of the edge weights should be used to ensure that the endpoints of an edges with higher weights are closer together (weights=1/E(g)$weight).

The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'. 'ggraph' natively supports the layout.

Value

matrix of xy coordinates

References

Gansner, E. R., Koren, Y., & North, S. (2004). Graph drawing by stress majorization. In International Symposium on Graph Drawing (pp. 239-250). Springer, Berlin, Heidelberg.

See Also

layout_stress3D

Examples

library(igraph)
set.seed(665)

g <- sample_pa(100, 1, 1, directed = FALSE)

# calculate layout manually
xy <- layout_with_stress(g)

# use it with ggraph
## Not run: 
library(ggraph)
ggraph(g, layout = "stress") +
    geom_edge_link0(edge_width = 0.2, colour = "grey") +
    geom_node_point(col = "black", size = 0.3) +
    theme_graph()

## End(Not run)

stress majorization layout in 3D

Description

force-directed graph layout based on stress majorization in 3D.

Usage

layout_with_stress3D(
  g,
  weights = NA,
  iter = 500,
  tol = 1e-04,
  mds = TRUE,
  bbox = 30
)

Arguments

Details

Be careful when using weights. In most cases, the inverse of the edge weights should be used to ensure that the endpoints of an edges with higher weights are closer together (weights=1/E(g)$weight).

Value

matrix of xyz coordinates

References

Gansner, E. R., Koren, Y., & North, S. (2004). Graph drawing by stress majorization. In International Symposium on Graph Drawing (pp. 239-250). Springer, Berlin, Heidelberg.

See Also

layout_stress

UMAP graph layouts

Description

Using the UMAP dimensionality reduction algorithm as a graph layout

Usage

layout_with_umap(g, pivots = NULL, ...)

layout_igraph_umap(g, circular, ...)

Arguments

Details

The layout_igraph_* function should not be used directly. It is only used as an argument for plotting with 'igraph'. UMAP can be tuned by many different parameters. Refer to the documentation at https://github.com/jlmelville/uwot for help

Value

matrix of xy coordinates

Author(s)

David Schoch

References

McInnes, Leland, John Healy, and James Melville. "Umap: Uniform manifold approximation and projection for dimension reduction." arXiv preprint arXiv:1802.03426 (2018).

Examples

library(igraph)

g <- sample_islands(10, 20, 0.6, 10)
# xy <- layout_with_umap(g, min_dist = 0.5)

Subway network of Berlin

Description

A dataset containing the subway network of Berlin

Usage

metro_berlin

Format

igraph object

References

Kujala, Rainer, et al. "A collection of public transport network data sets for 25 cities." Scientific data 5 (2018): 180089.

Multilevel example Network

Description

Multilevel network, where both levels have different structural features

Usage

multilvl_ex

Format

igraph object