observeNetwork: Observe a network partially according to a given sampling...

In missSBM: Handling Missing Data in Stochastic Block Models

Observe a network partially according to a given sampling design

Description

This function draws observations in an adjacency matrix according to a given network sampling design.

Usage

observeNetwork(
  adjacencyMatrix,
  sampling,
  parameters,
  clusters = NULL,
  covariates = list(),
  similarity = l1_similarity,
  intercept = 0
)

Arguments

adjacencyMatrix	The N x N adjacency matrix of the network to sample. The diagonal is expected to be NA (no self-loops); any other pre-existing NA entry is treated as an absent edge (coded 0) before sampling is applied on top of it, with a warning.
sampling	The sampling design used to observe the adjacency matrix, see details.
parameters	The sampling parameters (adapted to each sampling, see details).
clusters	An optional clustering membership vector of the nodes. Only necessary for block samplings.
covariates	An optional list with M entries (the M covariates). If the covariates are node-centered, each entry of covariates. must be a size-N vector; if the covariates are dyad-centered, each entry of covariates must be N x N matrix.
similarity	An optional function to compute similarities between node covariates. Default is l1_similarity, that is, -abs(x-y). Only relevant when the covariates are node-centered.
intercept	An optional intercept term to be added in case of the presence of covariates. Default is 0.

Details

Internal functions use future_lapply, so set your plan to 'multisession' or 'multicore' to use several cores/workers.

The different sampling designs are split into two families in which we find dyad-centered and node-centered samplings. See \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/01621459.2018.1562934")} for a complete description.

• Missing at Random (MAR)

  • dyad parameter = p = Prob(Dyad(i,j) is observed)

  • node parameter = p = Prob(Node i is observed)

  • covar-dyad": parameter = beta in R^M, such that Prob(Dyad (i,j) is observed) = logistic(parameter' covarArray (i,j, .))

  • covar-node": parameter = nu in R^M such that Prob(Node i is observed) = logistic(parameter' covarMatrix (i,)

  • snowball": parameter = number of waves with Prob(Node i is observed in the 1st wave)

• Missing Not At Random (MNAR)

  • double-standard parameter = (p0,p1) with p0 = Prob(Dyad (i,j) is observed | the dyad is equal to 0), p1 = Prob(Dyad (i,j) is observed | the dyad is equal to 1)

  • block-node parameter = c(p(1),...,p(Q)) and p(q) = Prob(Node i is observed | node i is in cluster q)

  • block-dyad parameter = c(p(1,1),...,p(Q,Q)) and p(q,l) = Prob(Edge (i,j) is observed | node i is in cluster q and node j is in cluster l)

Value

an adjacency matrix with the same dimension as the input, yet with additional NAs.

Examples

## SBM parameters
N <- 300 # number of nodes
Q <- 3   # number of clusters
pi <- rep(1,Q)/Q     # block proportion
theta <- list(mean = diag(.45,Q) + .05 ) # connectivity matrix

## simulate an unidrected binary SBM without covariate
sbm <- sbm::sampleSimpleSBM(N, pi, theta)

## Sample network data

# some sampling design and their associated parameters
sampling_parameters <- list(
   "dyad" = .3,
   "node" = .3,
   "double-standard" = c(0.4, 0.8),
   "block-node" = c(.3, .8, .5),
   "block-dyad" = theta$mean,
   "degree" = c(.01, .01),
   "snowball" = c(2,.1)
 )

observed_networks <- list()

for (sampling in names(sampling_parameters)) {
  observed_networks[[sampling]] <-
     missSBM::observeNetwork(
       adjacencyMatrix = sbm$networkData,
       sampling        = sampling,
       parameters      = sampling_parameters[[sampling]],
       clusters        = sbm$memberships
     )
}

missSBM documentation built on July 22, 2026, 1:07 a.m.