Sunbelt XXXI International Network for Social Network ... - INSNA

Link‐trace Sampling For Social Networks: Advances And ApplicationsKurant, Maciej; Gjoka, Minas; Butts, Carter T.; Markopoulou, AthinaCollecting Network DataStatistical Methods, Respondent‐driven Sampling, Online Networks, link‐trace sampling, random walksTHURS.PM1Link‐trace sampling methods, in which units are selected by iteratively following ties from currently selected units to other members of a target population(e.g., random walk sampling), are of increasing importance for researchers in a variety of fields. Although link‐trace methods are powerful tools for samplinghidden or hard‐to‐reach populations, using them in a principled manner poses many challenges. Among these are (i) problems of assessing convergence (i.e.,determining whether the tracing process is sufficiently close to the target distribution to cease sampling), (ii) sampling from populations where no onerelationshipnetwork is sufficiently well‐connected to permit convergence, and (iii) accelerating convergence in settings where simple procedures areinefficient. Here, we present practical methods for addressing each of these problems, drawing on ideas from the literature on Markov Chain Monte Carlosimulation and survey sampling. We demonstrate the efficacy of our techniques via application to several large online social networks, as well as simulationstudies. Some implications for approaches such as respondent‐driven sampling are also discussed.Local And Global Diversity In Networks And Systemic PerformanceLazer, David; Gomez, CharlesAgent‐Based Models and Multi‐Agent SystemsNetwork Dynamics, Problem Solving, Organization Theory, Agent Based Models, Exploration Versus ExploitationFRI.PM2The relationship between diversity and group problem solving has received substantial scholarly attention, often with the conclusion that diversity assists inmore thorough examination of potential solutions, with superior performance (e.g. Page). Is it better for individuals with different perspectives to be mixedtogether, allowing for the emergence of synergies among approaches? Far less attention has been paid to the impact of the distribution of a given level ofdiversity within a system. In other words, for a given level of global diversity, how much local diversity is desirable? Our analysis suggests a complex answer.We distinguish between three types of diversity: capacity, scope and foresight. Diversity in capacity refers to differences in capacity of agents to explorepossible solutions. Diversity in scope distinguishes agents in their capacity to explore a much broader search field at each starting point. Diversity in foresightallows agents to explore all possible solutions at any given starting point using their limited search capabilities. We find that with respect to capacity and toforesight, preserving local diversity is unambiguously best at all time scales. Local diversity enhances the system’s ability to see alternate pathways from anysolutions in the network. However, the benefits of preserving local scope diversity are unclear.