diffnet-class function

Creates a diffnet class object

diffnet objects contain difussion networks. With adjacency matrices and time of adoption (toa) vector as its main components, most of the package's functions have methods for this class of objects.

as_diffnet(graph, ...)

## Default S3 method:
as_diffnet(graph, ...)

## S3 method for class 'networkDynamic'
as_diffnet(graph, toavar, ...)

new_diffnet(
  graph,
  toa,
  t0 = min(toa, na.rm = TRUE),
  t1 = max(toa, na.rm = TRUE),
  vertex.dyn.attrs = NULL,
  vertex.static.attrs = NULL,
  id.and.per.vars = NULL,
  graph.attrs = NULL,
  undirected = getOption("diffnet.undirected"),
  self = getOption("diffnet.self"),
  multiple = getOption("diffnet.multiple"),
  name = "Diffusion Network",
  behavior = "Unspecified"
)

## S3 method for class 'diffnet'
as.data.frame(
  x,
  row.names = NULL,
  optional = FALSE,
  attr.class = c("dyn", "static"),
  ...
)

diffnet.attrs(
  graph,
  element = c("vertex", "graph"),
  attr.class = c("dyn", "static"),
  as.df = FALSE
)

diffnet.attrs(graph, element = "vertex", attr.class = "static") <- value

diffnet.toa(graph)

diffnet.toa(graph, i) <- value

## S3 method for class 'diffnet'
print(x, ...)

nodes(graph)

diffnetLapply(graph, FUN, ...)

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

## S3 method for class 'diffnet'
dimnames(x)

## S3 method for class 'diffnet'
t(x)

## S3 method for class 'diffnet'
dim(x)

is_undirected(x)

## S3 method for class 'diffnet'
is_undirected(x)

## Default S3 method:
is_undirected(x)

is_self(x)

## S3 method for class 'diffnet'
is_self(x)

## Default S3 method:
is_self(x)

is_multiple(x)

## S3 method for class 'diffnet'
is_multiple(x)

## Default S3 method:
is_multiple(x)

is_valued(x)

## S3 method for class 'diffnet'
is_valued(x)

## Default S3 method:
is_valued(x)

Arguments

• graph: A dynamic graph (see netdiffuseR-graphs).
• ...: Further arguments passed to the jmethod.
• toavar: Character scalar. Name of the variable that holds the time of adoption.
• toa: Numeric vector of size $n$. Times of adoption.
• t0: Integer scalar. Passed to toa_mat.
• t1: Integer scalar. Passed to toa_mat.
• vertex.dyn.attrs: Vertices dynamic attributes (see details).
• vertex.static.attrs: Vertices static attributes (see details).
• id.and.per.vars: A character vector of length 2. Optionally specified to check the order of the rows in the attribute data.
• graph.attrs: Graph dynamic attributes (not supported yet).
• undirected: Logical scalar. When TRUE only the lower triangle of the adjacency matrix will considered (faster).
• self: Logical scalar. When TRUE autolinks (loops, self edges) are allowed (see details).
• multiple: Logical scalar. When TRUE allows multiple edges.
• name: Character scalar. Name of the diffusion network (descriptive).
• behavior: Character scalar. Name of the behavior been analyzed (innovation).
• x: A diffnet object.
• row.names: Ignored.
• optional: Ignored.
• attr.class: Character vector/scalar. Indicates the class of the attribute, either dynamic ("dyn"), or static ("static").
• element: Character vector/scalar. Indicates what to retrieve/alter.
• as.df: Logical scalar. When TRUE returns a data.frame.
• value: In the case of diffnet.toa, replacement, otherwise see below.
• i: Indices specifying elements to replace. See Extract.
• FUN: a function to be passed to lapply
• object: A diffnet object.

Returns

A list of class diffnet with the following elements: - graph: A list of length $T$. Containing sparse square matrices of size $n$

and class `dgCMatrix`.

• toa: An integer vector of size $T$ with times of adoption.

• adopt, cumadopt: Numeric matrices of size $n*T$ as those returned by toa_mat.

• vertex.static.attrs: If not NULL, a data frame with $n$ rows with vertex static attributes.

• vertex.dyn.attrs: A list of length $T$ with data frames containing vertex attributes throught time (dynamic).

• graph.attrs: A data frame with $T$ rows.

• meta: A list of length 9 with the following elements:

  • type: Character scalar equal to "dynamic".
  • class: Character scalar equal to "list".
  • ids: Character vector of size $n$ with vertices' labels.
  • pers: Integer vector of size $T$.
  • nper: Integer scalar equal to $T$.
  • n: Integer scalar equal to $n$.
  • self: Logical scalar.
  • undirected: Logical scalar.
  • multiple: Logical scalar.
  • name: Character scalar.
  • behavior: Character scalar.

Details

diffnet objects hold both, static and dynamic vertex attributes. When creating diffnet objects, these can be specified using the arguments vertex.static.attrs and vertex.dyn.attrs; depending on whether the attributes to specify are static or dynamic, netdiffuseR currently supports the following objects:

Class	Dimension	Check sorting
Static attributes
matrix	with $n$ rows	id
data.frame	with $n$ rows	id
vector	of length $n$	-
Dynamic attributes
matrix	with $n * T$ rows	id , per
data.frame	with $n * T$ rows	id , per
vector	of length $n*T$	-
list	of length $T$ with matrices or data.frames of $n$ rows	id , per

The last column, Check sorting , lists the variables that the user should specify if he wants the function to check the order of the rows of the attributes (notice that this is not possible for the case of vectors). By providing the name of the vertex id variable, id, and the time period id variable, per, the function makes sure that the attribute data is presented in the right order. See the example below. If the user does not provide the names of the vertex id and time period variables then the function does not check the way the rows are sorted, further it assumes that the data is in the correct order.

The function is_undirected returns TRUE if the network is marked as undirected. In the case of diffnet objects, this information is stored in the meta element as undirected. The default method is to try to find an attribute called undirected, i.e., attr(x, "undirected"), if no attribute is found, then the function returns FALSE.

The functions is_self, is_valued, and is_multiple work exactly the same as is_undirected. diffnet networks are not valued.

Auxiliary functions

diffnet.attrs Allows retriving network attributes. In particular, by default returns a list of length $T$ with data frames with the following columns:

1. per Indicating the time period to which the observation corresponds.
2. toa Indicating the time of adoption of the vertex.
3. Further columns depending on the vertex and graph attributes.

Each vertex static attributes' are repeated $T$ times in total so that these can be binded (rbind) to dynamic attributes.

When as.df=TRUE, this convenience function is useful as it can be used to create event history (panel data) datasets used for model fitting.

Conversely, the replacement method allows including new vertex or graph attributes either dynamic or static (see examples below).

diffnet.toa(graph) works as an alias of graph$toa. The replacement method, diffnet.toa<- used as diffnet.toa(graph)<-..., is the right way of modifying times of adoption as when doing so it performs several checks on the time ranges, and recalculates adoption and cumulative adoption matrices using toa_mat.

nodes(graph) is an alias for graph$meta$ids.

Examples

# Creating a random graph
set.seed(123)
graph <- rgraph_ba(t=9)
graph <- lapply(1:5, function(x) graph)

# Pretty TOA
names(graph) <- 2001L:2005L
toa <- sample(c(2001L:2005L,NA), 10, TRUE)

# Creating diffnet object
diffnet <- new_diffnet(graph, toa)
diffnet
summary(diffnet)

# Plotting slice 4
plot(diffnet, t=4)

# ATTRIBUTES ----------------------------------------------------------------

# Retrieving attributes
diffnet.attrs(diffnet, "vertex", "static")

# Now as a data.frame (only static)
diffnet.attrs(diffnet, "vertex", "static", as.df = TRUE)

# Now as a data.frame (all of them)
diffnet.attrs(diffnet, as.df = TRUE)
as.data.frame(diffnet) # This is a wrapper

# Unsorted data -------------------------------------------------------------
# Loading example data
data(fakesurveyDyn)

# Creating a diffnet object
fs_diffnet <- survey_to_diffnet(
   fakesurveyDyn, "id", c("net1", "net2", "net3"), "toa", "group",
   timevar = "time", keep.isolates=TRUE, warn.coercion=FALSE)

# Now, we extract the graph data and create a diffnet object from scratch
graph <- fs_diffnet$graph
ids <- fs_diffnet$meta$ids
graph <- Map(function(g) {
  dimnames(g) <- list(ids,ids)
  g
  }, g=graph)
attrs <- diffnet.attrs(fs_diffnet, as.df=TRUE)
toa   <- diffnet.toa(fs_diffnet)

# Lets apply a different sorting to the data to see if it works
n <- nrow(attrs)
attrs <- attrs[order(runif(n)),]

# Now, recreating the old diffnet object (notice -id.and.per.vars- arg)
fs_diffnet_new <- new_diffnet(graph, toa=toa, vertex.dyn.attrs=attrs,
   id.and.per.vars = c("id", "per"))

# Now, retrieving attributes. The 'new one' will have more (repeated)
attrs_new <- diffnet.attrs(fs_diffnet_new, as.df=TRUE)
attrs_old <- diffnet.attrs(fs_diffnet, as.df=TRUE)

# Comparing elements!
tocompare <- intersect(colnames(attrs_new), colnames(attrs_old))
all(attrs_new[,tocompare] == attrs_old[,tocompare], na.rm = TRUE) # TRUE!

# diffnetLapply -------------------------------------------------------------

data(medInnovationsDiffNet)
diffnetLapply(medInnovationsDiffNet, function(x, cumadopt, ...) {sum(cumadopt)})

See Also

Default options are listed at netdiffuseR-options

Other diffnet methods: %*%(), as.array.diffnet(), c.diffnet(), diffnet-arithmetic, diffnet_index, plot.diffnet(), summary.diffnet()

Other data management functions: edgelist_to_adjmat(), egonet_attrs(), isolated(), survey_to_diffnet()

Author(s)

George G. Vega Yon