"Network Sampling with Memory" - Co-Sponsored with Duke Network Analysis Center (DNAC)
Sampling from a network using a random walk based approach such as Respondent Driven Sampling (RDS) is difficult because the sample can get stuck in isolated clusters of the network, reducing precision. In this paper we propose an alternative strategy Network Sampling with Memory (NSM) that uses social network data collected from respondents to increase the efficiency of the sampling process.