sample_about_one_seed {snowboot}

Snowball Sampling with Multiple Inclusions around One Network Node

Description

This function obtains a labeled snowball with multiple inclusions (LSMI) sample, starting from a single network node called seed. See Figure 1 by Thompson et al. (2016) illustrating the algorithm of sampling around one seed.

Usage

sample_about_one_seed(net, seed, n.wave = 1)

Arguments

net

a network object that is a list containing:

degree

the degree sequence of the network, which is an integer vector of length n;

edges

the edgelist, which is a two-column matrix, where each row is an edge of the network;

n

the network order (i.e., number of nodes in the network).

The network object can be simulated by random_network, selected from the networks available in artificial_networks, converged from an igraph object with igraph_to_network, etc.

seed

numeric ID of a seed to start the LSMI.

n.wave

an integer defining the number of waves (order of the neighborhood) to be recorded around the seed in the LSMI. For example, n.wave = 1 corresponds to an LSMI with the seed and its first neighbors. Note that the algorithm allows for multiple inclusions.

Value

sample_about_one_seed returns a list of length n.wave + 1 containing ID of the seed (1st element of the output list), IDs of nodes in the 1st wave (2nd element of the list), ..., IDs of nodes in the wave n.wave ((n.wave + 1)th element of the list). If a wave has no nodes in it, the corresponding element of the output contains NA.

References

Thompson ME, Ramirez Ramirez LL, Lyubchich V, Gel YR (2016). “Using the bootstrap for statistical inference on random graphs.” Canadian Journal of Statistics, 44(1), 3–24. doi: 10.1002/cjs.11271.

See Also

lsmi

Examples

net <- artificial_networks[[1]]
a <- sample_about_one_seed(net, seed = 1, n.wave = 2)