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Network Sampling
Network sampling designs select subsets of the nodes and relationships in a network for observation and study. Some network samples are drawn in order to estimate properties of a network—like density, centralization, or segmentation—or parameters of stochastic network models. Other network samples identify pools of subjects for survey studies about characteristics of a population of nodes. This entry describes some major network sampling designs employed in these ways and concludes with considerations for researchers when making inferences from network samples.
All network sampling designs select subsets of both the nodes and the relationships that comprise a network. Some designs explicitly sample nodes from a list or other sampling frame, and then observe them and the relationships in which they are involved. Others start by sampling relationships, then studying them together with the nodes they connect. Still others instead begin with an initial set of nodes and use referrals from that initial set to others in constructing a sample.
Node-Based Designs
One approach to sampling from a network begins by using some probability scheme to select a set of nodes from a sampling frame listing all nodes in the network of interest. “Induced subgraph” sampling then obtains data on relationships among all pairs of the sampled nodes. A “sampling of stars” or “egocentric network sampling” design instead measures the relationships between the sampled nodes and all nodes (sampled or unsampled) in the population. Subsequent data collection may or may not specifically identify the “alter” nodes related to a sampled node.
For a given number of sampled nodes, the egocentric design provides more information. Consider an undirected one-mode network in a school with 1,000 students, and hence (1,000)(999)/2 = 499,500 dyads (potential relationships). For a sample of 100 students (e.g., from school enrollment records), the induced subgraph design would collect data on (100)(99)/2 = 4,950 dyads. Egocentric network sampling would yield, in addition, information about the 100(900) = 90,000 dyads consisting of sampled and unsampled students.
Relationship-Based Designs
If a list of dyadic relationships (e.g., alliances between companies or international agreements) exists, one can sample from a one-mode network by selecting relationships from it. Those nodes involved in the sampled relationships would thereby be chosen. Such a design tends to select higher degree nodes—those involved in more relationships—requiring that suitable weights be employed.
Hypernetwork sampling extends this design to sampling from a two-mode network that depicts relationships between entities of two distinct types, say individuals and groups. If a sampling frame is available for one type of entity (say groups), a sample of elements may be drawn from it. Entities of the other type (individuals) affiliated with the sampled elements (groups) will thereby be selected, with probability proportional to their degree (number of group affiliations).
Link-Tracing Designs
Link-tracing designs develop a sample from a population by following connections between nodes. Several such sampling schemes exist, known by names including multiplicity sampling, random walk sampling, respondent-driven sampling, snowball sampling, and others. All of them begin by selecting—and collecting data from—one or more “seed” nodes; these data include information about links between the seeds and other nodes in the population. They then select one or more nodes to which each seed is related and add them to the sample. Such tracing may occur only once or be repeated by tracing links from the added nodes to still others, possibly several times. Link-tracing methods are often used when no listing of network nodes is available.
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