Package 'edgebundle' reference manual

Title:	Algorithms for Bundling Edges in Networks and Visualizing Flow and Metro Maps
Description:	Implements several algorithms for bundling edges in networks and flow and metro map layouts. This includes force directed edge bundling <doi:10.1111/j.1467-8659.2009.01450.x>, a flow algorithm based on Steiner trees<doi:10.1080/15230406.2018.1437359> and a multicriteria optimization method for metro map layouts <doi:10.1109/TVCG.2010.24>.
Authors:	David Schoch [aut, cre] (ORCID: <https://orcid.org/0000-0003-2952-4812>), Jose Luis Blanco-Claraco [cph] (Author of the bundled nanoflann C++ library)
Maintainer:	David Schoch <david@schochastics.net>
License:	MIT + file LICENSE
Version:	1.0.0
Built:	2026-10-04 11:54:33 UTC
Source:	https://github.com/schochastics/edgebundle

Migration from California in 2010

Description

A dataset containing the number of people who migrated from California to other US states

Usage

cali2010
cali2010

Format

igraph object

Source

https://www.census.gov/data/tables/time-series/demo/geographic-mobility/state-to-state-migration.html

Convert edges

Description

converts edges of an igraph/network/tidygraph object into format useable for edge bundling

Usage

convert_edges(object, coords)

## Default S3 method:
convert_edges(object, coords)

## S3 method for class 'igraph'
convert_edges(object, coords)

## S3 method for class 'network'
convert_edges(object, coords)

## S3 method for class 'tbl_graph'
convert_edges(object, coords)
convert_edges(object, coords)

## Default S3 method:
convert_edges(object, coords)

## S3 method for class 'igraph'
convert_edges(object, coords)

## S3 method for class 'network'
convert_edges(object, coords)

## S3 method for class 'tbl_graph'
convert_edges(object, coords)

Arguments

object

graph object

coords

coordinates of vertices

Value

data frame of edges with coordinates

Author(s)

David Schoch

Dispatches to one of the edge bundling algorithms by name. This is a thin wrapper over the individual ⁠edge_bundle_*()⁠ functions and mirrors the way frontends (e.g. vellumplot) select a bundling method.

Usage

edge_bundle(
  object,
  xy,
  type = c("force", "divided", "stub", "path", "hammer", "mingle"),
  ...
)
edge_bundle(
  object,
  xy,
  type = c("force", "divided", "stub", "path", "hammer", "mingle"),
  ...
)

Arguments

object

a graph object (igraph/network/tbl_graph)

xy

coordinates of vertices

type

bundling algorithm: "force" (force-directed, Holten), "divided" (force-directed for directed graphs, Selassie et al.), "stub" (Nocaj & Brandes), "path" (edge-path, Wallinger et al.), "hammer" (KDE, Hurter et al.), or "mingle" (Gansner et al.)

...

arguments passed on to the selected ⁠edge_bundle_*()⁠ function

Value

data.frame containing the bundled edges (x, y, index, group)

Author(s)

David Schoch

Examples

library(igraph)
g <- graph_from_edgelist(
    matrix(c(1, 12, 2, 11, 3, 10, 4, 9, 5, 8, 6, 7), ncol = 2, byrow = TRUE), FALSE
)
xy <- cbind(c(rep(0, 6), rep(1, 6)), c(1:6, 1:6))
edge_bundle(g, xy, type = "force")
library(igraph)
g <- graph_from_edgelist(
    matrix(c(1, 12, 2, 11, 3, 10, 4, 9, 5, 8, 6, 7), ncol = 2, byrow = TRUE), FALSE
)
xy <- cbind(c(rep(0, 6), rep(1, 6)), c(1:6, 1:6))
edge_bundle(g, xy, type = "force")

force directed edge bundling

Description

Implements the classic edge bundling by Holten.

Usage

edge_bundle_force(
  object,
  xy,
  K = 1,
  C = 6,
  P = 1,
  S = 0.04,
  P_rate = 2,
  I = 50,
  I_rate = 2/3,
  compatibility_threshold = 0.6,
  eps = 1e-08,
  directed = FALSE,
  ...
)
edge_bundle_force(
  object,
  xy,
  K = 1,
  C = 6,
  P = 1,
  S = 0.04,
  P_rate = 2,
  I = 50,
  I_rate = 2/3,
  compatibility_threshold = 0.6,
  eps = 1e-08,
  directed = FALSE,
  ...
)

Arguments

object

a graph object (igraph/network/tbl_graph)

xy

coordinates of vertices

K

spring constant

C

number of iteration cycles

P

number of initial edge divisions

S

initial step size

P_rate

rate of edge divisions

I

number of initial iterations

I_rate

rate of iteration decrease per cycle

compatibility_threshold

threshold for when edges are considered compatible

eps

accuracy

directed

logical. If TRUE, use divided edge bundling (Selassie et al. 2011) which keeps edges running in opposite directions in separate lanes. Requires an igraph/tbl_graph object with edge directions.

...

arguments passed to the divided bundler when directed = TRUE: lane_width, k_spring, k_charge, decay, friction, step, passes, iterations, use_connectivity.

Details

This is a re-implementation of https://github.com/upphiminn/d3.ForceBundle. Force directed edge bundling is slow (O(E^2)).

see online for plotting tips

Value

data.frame containing the bundled edges

Author(s)

David Schoch

References

Holten, Danny, and Jarke J. Van Wijk. "Force-Directed Edge Bundling for Graph Visualization." Computer Graphics Forum (Blackwell Publishing Ltd) 28, no. 3 (2009): 983-990.

Selassie, David, Brandon Heller, and Jeffrey Heer. "Divided edge bundling for directional network data." IEEE Transactions on Visualization and Computer Graphics 17, no. 12 (2011): 2354-2363.

Examples

library(igraph)
g <- graph_from_edgelist(
    matrix(c(
        1, 12, 2, 11, 3, 10,
        4, 9, 5, 8, 6, 7
    ), ncol = 2, byrow = TRUE), FALSE
)
xy <- cbind(c(rep(0, 6), rep(1, 6)), c(1:6, 1:6))
edge_bundle_force(g, xy)
library(igraph)
g <- graph_from_edgelist(
    matrix(c(
        1, 12, 2, 11, 3, 10,
        4, 9, 5, 8, 6, 7
    ), ncol = 2, byrow = TRUE), FALSE
)
xy <- cbind(c(rep(0, 6), rep(1, 6)), c(1:6, 1:6))
edge_bundle_force(g, xy)

hammer edge bundling

Description

Implements hammer edge bundling via kernel density estimation (KDEEB).

Usage

edge_bundle_hammer(
  object,
  xy,
  bw = 0.05,
  decay = 0.7,
  npoints = 50,
  iterations = 12,
  grid = 256,
  step = 0.6,
  smooth = 1
)
edge_bundle_hammer(
  object,
  xy,
  bw = 0.05,
  decay = 0.7,
  npoints = 50,
  iterations = 12,
  grid = 256,
  step = 0.6,
  smooth = 1
)

Arguments

object

a graph object (igraph/network/tbl_graph)

xy

coordinates of vertices

bw

initial bandwidth (fraction of the layout extent)

decay

bandwidth decay per iteration (0-1)

npoints

number of points sampled per edge

iterations

number of bundling iterations

grid

resolution of the density grid

step

advection step size (multiple of the bandwidth)

smooth

number of smoothing passes per iteration

Details

Native re-implementation of the KDE-based bundling behind the datashader "hammer" bundler, following Hurter, Ersoy and Telea (2012). Earlier versions of this function wrapped datashader through reticulate; it now runs entirely in C++ with no Python dependency. Edges are sampled into points, a density field is estimated on a grid, points are advected up the density gradient, and the bandwidth shrinks each iteration so bundles emerge along density ridges.

see online for plotting tips

Value

data.frame containing the bundled edges

Author(s)

David Schoch

References

Hurter, Christophe, Ozan Ersoy, and Alexandru Telea. "Graph Bundling by Kernel Density Estimation." Computer Graphics Forum 31, no. 3 (2012): 865-874.

Examples

library(igraph)
g <- graph_from_edgelist(
    matrix(c(1, 12, 2, 11, 3, 10, 4, 9, 5, 8, 6, 7), ncol = 2, byrow = TRUE), FALSE
)
xy <- cbind(c(rep(0, 6), rep(1, 6)), c(1:6, 1:6))
edge_bundle_hammer(g, xy)
library(igraph)
g <- graph_from_edgelist(
    matrix(c(1, 12, 2, 11, 3, 10, 4, 9, 5, 8, 6, 7), ncol = 2, byrow = TRUE), FALSE
)
xy <- cbind(c(rep(0, 6), rep(1, 6)), c(1:6, 1:6))
edge_bundle_hammer(g, xy)

MINGLE edge bundling

Description

Multilevel agglomerative edge bundling (MINGLE).

Usage

edge_bundle_mingle(object, xy, k = 10, segments = 50, bundle_strength = 0.9)
edge_bundle_mingle(object, xy, k = 10, segments = 50, bundle_strength = 0.9)

Arguments

object

a graph object (igraph/network/tbl_graph)

xy

coordinates of vertices

k

number of nearest neighbours considered as merge candidates per edge

segments

number of points sampled per bundled edge

bundle_strength

strength of bundling between 0 (straight edges) and 1 (route fully through the meeting points)

Details

Groups edges bottom-up: two bundles are merged whenever routing them through shared meeting points reduces the total drawn length ("ink"). Meeting points are the geometric medians of the source-side and target-side endpoints. A kNN proximity graph over the edges is built once with a kd-tree and then coarsened level by level (O(E log E)).

see online for plotting tips

Value

data.frame containing the bundled edges

Author(s)

David Schoch

References

Gansner, Emden R., Yifan Hu, Stephen North, and Carlos Scheidegger. "Multilevel agglomerative edge bundling for visualizing large graphs." 2011 IEEE Pacific Visualization Symposium (2011): 187-194.

Examples

library(igraph)
g <- graph_from_edgelist(
    matrix(c(1, 12, 2, 11, 3, 10, 4, 9, 5, 8, 6, 7), ncol = 2, byrow = TRUE), FALSE
)
xy <- cbind(c(rep(0, 6), rep(1, 6)), c(1:6, 1:6))
edge_bundle_mingle(g, xy)
library(igraph)
g <- graph_from_edgelist(
    matrix(c(1, 12, 2, 11, 3, 10, 4, 9, 5, 8, 6, 7), ncol = 2, byrow = TRUE), FALSE
)
xy <- cbind(c(rep(0, 6), rep(1, 6)), c(1:6, 1:6))
edge_bundle_mingle(g, xy)

Edge-Path Bundling

Description

Implements edge-path bundling.

Usage

edge_bundle_path(
  g,
  xy,
  max_distortion = 2,
  weight_fac = 2,
  segments = 20,
  bundle_strength = 1,
  mode = "out"
)
edge_bundle_path(
  g,
  xy,
  max_distortion = 2,
  weight_fac = 2,
  segments = 20,
  bundle_strength = 1,
  mode = "out"
)

Arguments

g

an igraph object

xy

coordinates of vertices

max_distortion

maximum distortion

weight_fac

edge weight factor

segments

number of subdivisions of edges

bundle_strength

bundle strength factor

mode

the parameter fo shortest_paths

Details

This is a re-implementation of https://github.com/mwallinger-tu/edge-path-bundling

see online for plotting tips

Value

data.frame containing the bundled edges

Author(s)

David Schoch

References

Wallinger, M., Archambault, D., Auber, D., Nollenburg, M., & Peltonen, J. (2021). Edge-Path Bundling: A Less Ambiguous Edge Bundling Approach. IEEE Transactions on Visualization and Computer Graphics.

Examples

library(igraph)
g <- graph_from_edgelist(matrix(c(
    1, 2, 1, 6,
    1, 4, 2, 3, 3, 4, 4, 5, 5, 6
), ncol = 2, byrow = TRUE), FALSE)
xy <- cbind(c(0, 10, 25, 40, 50, 50), c(0, 15, 25, 15, 0, -10))
edge_bundle_path(g, xy)
library(igraph)
g <- graph_from_edgelist(matrix(c(
    1, 2, 1, 6,
    1, 4, 2, 3, 3, 4, 4, 5, 5, 6
), ncol = 2, byrow = TRUE), FALSE)
xy <- cbind(c(0, 10, 25, 40, 50, 50), c(0, 15, 25, 15, 0, -10))
edge_bundle_path(g, xy)

stub edge bundling

Description

Implements the stub edge bundling by Nocaj and Brandes

Usage

edge_bundle_stub(
  object,
  xy,
  alpha = 11,
  beta = 75,
  gamma = 40,
  t = 0.5,
  tshift = 0.5
)
edge_bundle_stub(
  object,
  xy,
  alpha = 11,
  beta = 75,
  gamma = 40,
  t = 0.5,
  tshift = 0.5
)

Arguments

object

a graph object (igraph/tbl_graph). Does not support network objects

xy

coordinates of vertices

alpha

maximal angle (in degree) between consecutive edges in a bundle

beta

angle (in degree) at which to connect two stubs

gamma

maximal overall angle (in degree) of an edge bundle

t

numeric between 0 and 1. control point location

tshift

numeric between 0 and 1. The closer to one, the longer the bigger bundle

Details

see online for plotting tips

Value

data.frame containing the bundled edges

Author(s)

David Schoch

References

Nocaj, Arlind, and Ulrik Brandes. "Stub bundling and confluent spirals for geographic networks." International Symposium on Graph Drawing. Springer, Cham, 2013.

Examples

library(igraph)
g <- make_star(10, "undirected")

xy <- matrix(c(
    0, 0,
    cos(90 * pi / 180), sin(90 * pi / 180),
    cos(80 * pi / 180), sin(80 * pi / 180),
    cos(70 * pi / 180), sin(70 * pi / 180),
    cos(330 * pi / 180), sin(330 * pi / 180),
    cos(320 * pi / 180), sin(320 * pi / 180),
    cos(310 * pi / 180), sin(310 * pi / 180),
    cos(210 * pi / 180), sin(210 * pi / 180),
    cos(200 * pi / 180), sin(200 * pi / 180),
    cos(190 * pi / 180), sin(190 * pi / 180)
), ncol = 2, byrow = TRUE)

edge_bundle_stub(g, xy)
# use ggforce::geom_bezier for plotting
library(igraph)
g <- make_star(10, "undirected")

xy <- matrix(c(
    0, 0,
    cos(90 * pi / 180), sin(90 * pi / 180),
    cos(80 * pi / 180), sin(80 * pi / 180),
    cos(70 * pi / 180), sin(70 * pi / 180),
    cos(330 * pi / 180), sin(330 * pi / 180),
    cos(320 * pi / 180), sin(320 * pi / 180),
    cos(310 * pi / 180), sin(310 * pi / 180),
    cos(210 * pi / 180), sin(210 * pi / 180),
    cos(200 * pi / 180), sin(200 * pi / 180),
    cos(190 * pi / 180), sin(190 * pi / 180)
), ncol = 2, byrow = TRUE)

edge_bundle_stub(g, xy)
# use ggforce::geom_bezier for plotting

Spiral flow tree

Description

Computes a one-to-many flow map as an angle-restricted spiral tree (Verbeek, Buchin and Speckmann 2011). This is the recommended flow map layout; unlike tnss_tree it needs no dummy nodes or triangulation, keeps the real node positions fixed, produces a planar (crossing-free) tree with smooth, shallow-angle merges, and is tuned by a single parameter alpha.

Usage

flow_tree(object, xy, root, alpha = 40, n = 20, optimize = FALSE)
flow_tree(object, xy, root, alpha = 40, n = 20, optimize = FALSE)

Arguments

object

a one-to-many flow network (star graph) as igraph/tbl_graph, with a weight edge attribute giving the flow

xy

coordinates of vertices

root

root node id of the flow

alpha

restricting angle in degrees (0-90). Smaller values bundle more tightly toward the root; typical values are 20-45.

n

number of points sampled per tree edge

optimize

logical. If TRUE, refine the tree with an approximate FLOWTREE optimization (Verbeek et al. 2011, section 5): join points stay fixed while edge interiors are relaxed for smoothness and to keep clear of node obstacles. This can slightly relax the strict alpha bound.

Value

data.frame with columns x, y, flow and edge (one smooth arc per tree edge)

Author(s)

David Schoch

References

Verbeek, Kevin, Kevin Buchin, and Bettina Speckmann. "Flow map layout via spiral trees." IEEE Transactions on Visualization and Computer Graphics 17, no. 12 (2011): 2536-2545.

Examples

xy <- cbind(state.center$x, state.center$y)[!state.name %in% c("Alaska", "Hawaii"), ]
flow <- flow_tree(cali2010, xy, root = 4, alpha = 40)
xy <- cbind(state.center$x, state.center$y)[!state.name %in% c("Alaska", "Hawaii"), ]
flow <- flow_tree(cali2010, xy, root = 4, alpha = 40)

Subway network of Berlin

Description

A dataset containing the subway network of Berlin

Usage

metro_berlin
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.

Metro Map Layout (deprecated)

Description

Metro map layout based on multicriteria optimization. Deprecated: use graphlayouts::layout_as_metromap() instead, which now provides this layout.

Usage

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

Arguments

object

original graph

xy

initial layout of the original graph

l

desired multiple of grid point spacing. (l*gr determines desired edge length)

gr

grid spacing. (l*gr determines desired edge length)

w

weight vector for criteria (see details)

bsize

number of grid points a station can move away rom its original position

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
Octiinearity Criterion: Each edge should be drawn horizontally, vertically, or diagonally at 45 degree, so we penalize edges that are not at a desired angle see online for more plotting tips

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
xy_new <- metro_multicriteria(g, xy, l = 2, gr = 0.5, w = c(100, 100, 1, 1, 100), bsize = 35)
# 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
xy_new <- metro_multicriteria(g, xy, l = 2, gr = 0.5, w = c(100, 100, 1, 1, 100), bsize = 35)

Sample points for triangulated networks

Description

uses various sampling strategies to create dummy nodes for the tnss_tree

Usage

tnss_dummies(
  xy,
  root,
  circ = TRUE,
  line = TRUE,
  diag = TRUE,
  grid = FALSE,
  rand = FALSE,
  ncirc = 9,
  rcirc = 2,
  nline = 10,
  ndiag = 50,
  ngrid = 50,
  nrand = 50
)
tnss_dummies(
  xy,
  root,
  circ = TRUE,
  line = TRUE,
  diag = TRUE,
  grid = FALSE,
  rand = FALSE,
  ncirc = 9,
  rcirc = 2,
  nline = 10,
  ndiag = 50,
  ngrid = 50,
  nrand = 50
)

Arguments

xy

coordinates of "real" nodes

root

root node id

circ

logical. create circular dummy nodes around leafs.

line

logical. create dummy nodes on a straight line between root and leafs.

diag

logical. create dummy nodes diagonally through space.

grid

logical. create dummy nodes on a grid.

rand

logical. create random dummy nodes.

ncirc

numeric. number of circular dummy nodes per leaf.

rcirc

numeric. radius of circles around leaf nodes.

nline

numeric. number of straight line nodes per leaf.

ndiag

numeric. number of dummy nodes on diagonals.

ngrid

numeric. number of dummy nodes per dim on grid.

nrand

numeric. number of random nodes to create.

Value

coordinates of dummy nodes

Author(s)

David Schoch

Examples

# dummy nodes for tree rooted in California
xy <- cbind(state.center$x, state.center$y)
xy_dummy <- tnss_dummies(xy, 4)
# dummy nodes for tree rooted in California
xy <- cbind(state.center$x, state.center$y)
xy_dummy <- tnss_dummies(xy, 4)

Smooth a Steiner tree

Description

Converts the Steiner tree to smooth paths

Usage

tnss_smooth(g, bw = 3, n = 10)
tnss_smooth(g, bw = 3, n = 10)

Arguments

g

Steiner tree computed with tnss_tree

bw

bandwidth of Gaussian Kernel

n

number of extra nodes to include per edge

Details

see see online for tips on plotting the result

Value

data.frame containing the smoothed paths

Author(s)

David Schoch

Examples

xy <- cbind(state.center$x, state.center$y)[!state.name %in% c("Alaska", "Hawaii"), ]
xy_dummy <- tnss_dummies(xy, root = 4)
gtree <- tnss_tree(cali2010, xy, xy_dummy, root = 4, gamma = 0.9)
tree_smooth <- tnss_smooth(gtree, bw = 10, n = 10)
xy <- cbind(state.center$x, state.center$y)[!state.name %in% c("Alaska", "Hawaii"), ]
xy_dummy <- tnss_dummies(xy, root = 4)
gtree <- tnss_tree(cali2010, xy, xy_dummy, root = 4, gamma = 0.9)
tree_smooth <- tnss_smooth(gtree, bw = 10, n = 10)

Create Steiner tree from real and dummy points

Description

Creates an approximated Steiner tree for a flow map visualization. For most purposes flow_tree is now the recommended flow map layout (planar, angle-restricted, no dummy nodes); tnss_tree is kept as an alternative and requires the interp package for triangulation.

Usage

tnss_tree(
  g,
  xy,
  xydummy,
  root,
  gamma = 0.9,
  epsilon = 0.3,
  elen = Inf,
  order = "random"
)
tnss_tree(
  g,
  xy,
  xydummy,
  root,
  gamma = 0.9,
  epsilon = 0.3,
  elen = Inf,
  order = "random"
)

Arguments

g

original flow network (must be a one-to-many flow network, i.e star graph). Must have a weight attribute indicating the flow

xy

coordinates of "real" nodes

xydummy

coordinates of "dummy" nodes

root

root node id of the flow

gamma

edge length decay parameter

epsilon

percentage of points keept on a line after straightening with Visvalingam Algorithm

elen

maximal length of edges in triangulation

order

in which order shortest paths are calculated ("random","weight","near","far")

Details

Use tnss_smooth to smooth the edges of the tree

Value

approximated Steiner tree from dummy and real nodes as igraph object

Author(s)

David Schoch

References

Sun, Shipeng. "An automated spatial flow layout algorithm using triangulation, approximate Steiner tree, and path smoothing." AutoCarto, 2016.

Examples

xy <- cbind(state.center$x, state.center$y)[!state.name %in% c("Alaska", "Hawaii"), ]
xy_dummy <- tnss_dummies(xy, root = 4)
gtree <- tnss_tree(cali2010, xy, xy_dummy, root = 4, gamma = 0.9)
xy <- cbind(state.center$x, state.center$y)[!state.name %in% c("Alaska", "Hawaii"), ]
xy_dummy <- tnss_dummies(xy, root = 4)
gtree <- tnss_tree(cali2010, xy, xy_dummy, root = 4, gamma = 0.9)

Flights within the US

Description

A dataset containing flights between US airports as igraph object

Usage

us_flights
us_flights

Format

igraph object

Source

https://gist.githubusercontent.com/mbostock/7608400/raw

Migration within the US 2010-2019

Description

A dataset containing the number of people migrating between US states from 2010-2019

Usage

us_migration
us_migration

Format

data.frame

Source

https://www.census.gov/data/tables/time-series/demo/geographic-mobility/state-to-state-migration.html

Package 'edgebundle'

Help Index

Migration from California in 2010

Description

Usage

Format

Source

Convert edges

Description

Usage

Arguments

Value

Author(s)

Edge bundling

Description

Usage

Arguments

Value

Author(s)

See Also

Examples

force directed edge bundling

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

hammer edge bundling

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

MINGLE edge bundling

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Edge-Path Bundling

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

stub edge bundling

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Spiral flow tree

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples