netplot In a nutshell

• What: An R package for network visualization inspired by Gephi.

• Why: Opinionated way to visualize graphs.¹

• Where: You can get the dev version on GitHub (USCCANA/netplot) or the stable version on CRAN.

netplot In a nutshell (cont.)

Current parameters in nplot (main function)

nplot(
  edgelist,
  layout,
  vertex.size             = 1,
  vertex.nsides           = 10,
  vertex.color            = grDevices::hcl.colors(1),
  vertex.size.range       = c(0.01, 0.03),
  vertex.frame.color      = ...,
  vertex.rot              = 0,
  vertex.frame.prop       = 0.2,
  vertex.label            = NULL,
  vertex.label.fontsize   = NULL,
  vertex.label.color      = "black",
  vertex.label.fontfamily = "HersheySans",
  vertex.label.fontface   = "bold",
  vertex.label.show       = 0.3,
  vertex.label.range      = c(5, 15)
)

netplot In a nutshell (cont.)

Current parameters in nplot (main function)

nplot(
  edgelist,
  layout,
  vertex.size             = 1,
  vertex.nsides           = 10,
  vertex.color            = grDevices::hcl.colors(1),
  vertex.size.range       = c(0.01, 0.03),
  vertex.frame.color      = ...,
  vertex.rot              = 0,
  vertex.frame.prop       = 0.2,
  vertex.label            = NULL,
  vertex.label.fontsize   = NULL,
  vertex.label.color      = "black",
  vertex.label.fontfamily = "HersheySans",
  vertex.label.fontface   = "bold",
  vertex.label.show       = 0.3,
  vertex.label.range      = c(5, 15),
  edge.width              = 1,
  edge.width.range        = c(1, 2),
  edge.arrow.size         = NULL,
  edge.color              = ~ego(alpha = 0.25, col = "gray") + alter,
  edge.curvature          = pi/3,
  edge.line.lty           = "solid",
  edge.line.breaks        = 5,
  sample.edges            = 1
)

netplot In a nutshell (cont.)

Current parameters in nplot (main function)

nplot(
  edgelist,
  layout,
  vertex.size             = 1,
  vertex.nsides           = 10,
  vertex.color            = grDevices::hcl.colors(1),
  vertex.size.range       = c(0.01, 0.03),
  vertex.frame.color      = ...,
  vertex.rot              = 0,
  vertex.frame.prop       = 0.2,
  vertex.label            = NULL,
  vertex.label.fontsize   = NULL,
  vertex.label.color      = "black",
  vertex.label.fontfamily = "HersheySans",
  vertex.label.fontface   = "bold",
  vertex.label.show       = 0.3,
  vertex.label.range      = c(5, 15),
  edge.width              = 1,
  edge.width.range        = c(1, 2),
  edge.arrow.size         = NULL,
  edge.color              = ~ego(alpha = 0.25, col = "gray") + alter,
  edge.curvature          = pi/3,
  edge.line.lty           = "solid",
  edge.line.breaks        = 5,
  sample.edges            = 1,
  bg.col                  = "transparent",
  skip.vertex             = FALSE,
  skip.edges              = FALSE,
  skip.arrows             = skip.edges,
  add                     = FALSE,
  zero.margins            = TRUE,
  ...
)

Main features

Main features (cont.)

Example code+output

The personal friendship network of a faculty of a UK university, consisting of 81 vertices (individuals) and 817 directed and weighted connections. The school affiliation of each individual is stored as a vertex attribute. This dataset can serve as a testbed for community detection algorithms. (source: R package igraphdata)

How?

We start by loading data + colors…

library(netplot)
library(igraph)
data("UKfaculty", package="igraphdata")

# Vertex colors f(group)
vcols <- V(UKfaculty)$Group 
vcols <- palette.colors(
  n       = length(unique(vcols)),
  palette = "Alphabet"
)[vcols]

How?

We start by loading data + colors… and then plot!

library(netplot)
library(igraph)
data("UKfaculty", package="igraphdata")

# Vertex colors f(group)
vcols <- V(UKfaculty)$Group 
vcols <- palette.colors(
  n       = length(unique(vcols)),
  palette = "Alphabet"
)[vcols]

# Netplot call
set.seed(323)
nplot(
  UKfaculty,
  vertex.color = vcols
  )

Not a simple drawing

• Graphical objects (Grobs) are the building blocks in grid

• Benefit: Plots are not mere figures, are modifiable objects.

• netplot objects are a collection of grobs:

List of 11
 $ .xlim        : num [1:2] -1 1
 $ .ylim        : num [1:2] -0.5 0.5
 $ .layout      : num [1:81, 1:2] 0.6661 0.0201 0.7327 0.5399 -0.4903 ...
 $ .edgelist    : num [1:817, 1:2] 57 76 12 43 28 58 7 40 5 48 ...
 $ .N           : int 81
 $ .M           : int 817
 $ name         : chr "graph.2"
 $ gp           : NULL
 $ vp           : NULL
 $ children     :List of 2
  ..$ background:List of 10
  .. ..- attr(*, "class")= chr [1:3] "rect" "grob" "gDesc"
  ..$ graph     :List of 5
  .. ..- attr(*, "class")= chr [1:3] "gTree" "grob" "gDesc"
  ..- attr(*, "class")= chr "gList"
 $ childrenOrder: chr [1:2] "background" "graph"
 - attr(*, "class")= chr [1:4] "netplot" "gTree" "grob" "gDesc"

Example 2: Playing with patterns

netplot supports advanced patterns. The figures feature radial gradients (vertices), lineal gradients, and repeated patterns (background).

Alternatives

Alternatives (patterns)

barry in a nutshell

“Your go-to motif accountant”¹

Main features

DEFMs

DEFMs as ERGMs

A key component for fitting these models are change statistics.

Features: Change statistics

Let’s look into the change statistics edgecount, triangles, and gender-homophily when we remove tie 33-69.

Features: Change statistics (cont.)

Features: Memory and Comp. Efficiency

Building DEFMs

flowchart LR
  start[Create the<br>Model] --> count[Add<br>Counters]
  count --> count_done{Done?}
  count_done --> |Yes| const[Add\nconstraints]
  count_done --> |No| count
  const --> const_done{Done?}
  const_done --> |Yes| init((End))
  const_done --> |No| const

flowchart LR
  start[Hash the\narray] --> exists{Present?}
  exists --> |Yes| ptr[[Link\nPointer]]
  exists --> |No| addarray[[Compute\nsupport]]
  addarray --> save[[Save\nsupport]]
  ptr --> End((End))
  save --> End

Computing support

flowchart LR
  start[[Static<br>constraints]] --> emptymat[Initialize<br>array]
  emptymat --> edges[Set edge<br>sequence]
  edges --> startc((Start<br>counting))

flowchart LR
  nextes((Start)) --> anyleft{Any edge<br>left}
  anyleft --> |Yes| adde[Add edge]
  anyleft --> |No| ende((End))
  adde --> change[[Run<br>counters]]
  change --> indyn{Within dyn<br>constraint?}
  indyn --> |Yes| save[Include] -->anyleft
  indyn --> |No| anyleft

Current implemented models

In principle, any dataset we can represent as 0/1 arrays can be modeled with barry. We have applications in social networks, phylogenetics, and health outcomes.

Current implemented models: DEFMs

• DEFMs for multiple correlated outcomes (Markov Random Fields; on development with Drs. MJ Pugh and Tom Valente.)

Current implemented models: Social networks

• Motif counting applied to counting “imaginary motifs” in Cognitive Social Structures [CSS] (with Dr. Kyosuke Tanaka, submitted to Social Networks).

Current implemented models: Phylogenetics

• Modeling the evolution of gene functions in terms of transition between functional states (research grant submitted to National Human Genome Research Institute NHGRI).

Other projects

fmcmc | ergmito | aphylo | netdiffuseR | ABCoptim
slurmR | barry | rgexf | geese

Other projects (cont.)

Thanks!

A1. Change statistics formals

• The change statistic is defined as a real-valued vector where the \(k\)-th entry equals the observed change when the \(ij\)-th tie is removed from the network. Formally:

  \[ \delta(y_{ij}: 0\to 1) = s(\mathbf{y})_{ij}^+ - s(\mathbf{y})_{ij}^- \]

  Where \(s(\cdot)\) is a function returning graph \(\mathbf{y}\)’s observed statistics, and \(s(\mathbf{y})_{ij}^+\) represents its value when \(y_{ij} = 1\).

A1. Change statistics formals (cont.)

Furthermore, conditioning on the rest of the network (or array,) a tie transition probability equates a logit:

\[\begin{equation} \mbox{logit}\left({\mathbb{P}\left(y_{ij} = 1|y_{-ij}\right) }\right) = {\theta}^\mathbf{t}\Delta\delta\left(y_{ij}:0\to 1\right), \end{equation}\]

\[\begin{equation} {\mathbb{P}\left(y_{ij} = 1|y_{-ij}\right) } = \frac{1}{1 + \mbox{exp}\left\{-{\theta}^\mathbf{t}\Delta\delta\left(y_{ij}:0\to 1\right)\right\}} \end{equation}\]