# sample_about_one_seed: Snowball Sampling with Multiple Inclusions around One Network... In snowboot: Bootstrap Methods for Network Inference

## Description

This function obtains a labeled snowball with multiple inclusions (LSMI) sample, starting from a single network node called seed. See Figure 1 by \insertCitethompson_etal_2016;textualsnowboot illustrating the algorithm of sampling around one seed.

## Usage

 `1` ```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`.

\insertAllCited

`lsmi`

## Examples

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

snowboot documentation built on April 26, 2020, 1:05 a.m.