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

Exponential‐family Random Graph Models For Weighted NetworksKrivitsky, Pavel N.Exponential Random GraphsERGM/P*, Weighted Links, Transitivity, Exponential‐family Random Graph Models, Weighted Networks, count dataSUN.AM1Exponential‐family random graph models (ERGMs) provide a principled and flexible way to model and simulate features common in social networks, such aspropensities for homophily, mutuality, and friend‐of‐a‐friend triad closure, through choice of model terms (sufficient statistics). However, those ERGMsmodeling the more complex features (i.e. those which do not assume independence of dyads) have, to date, been limited to binary data: presence or absenceof ties. Thus, using ERGMs to analyze weighted networks, such as those in which counts of interactions or measurements of relationship strength wereobserved, has necessitated dichotomizing them, losing information. In this work, we generalize ERGMs to weighted graphs. Using the concept of referencemeasures, we describe a rigorous yet intuitive framework that retains many of the inferential and interpretability properties of the binary case, and discussadditional issues and caveats that emerge. Focusing on modeling counts, we introduce terms that generalize and model common social network features forcount data, while avoiding degeneracy. We demonstrate these methods on a commonly analyzed dataset whose weights are counts.Extending Measurements Of Opportunity Structures With Tetradic Substructures In Multilevel NetworksLazega, Emmanuel; Jourda, Marie; Mounier, Lise ; Lazega, DavidAnalyzing Network Data / BlockmodelingScientific Networks, Social Capital, Multilevel Networks, Performance, Advice Network, Interorganizational NetworksSAT.AM2This paper extends network measurements of opportunity structures by contributing to research on multi‐level network analysis and by exploring thearticulation of inter‐individual and inter‐organizational networks. Following a ‘linked‐design’ approach, we look at the specific value of potential, indirect tiesthat can be added to members’ observed social network via their organization’s inter‐organizational network. We measure the extent to which, and theconditions under which, this “augmented” social network provides more social capital to members than their observed inter‐individual networks. Our datasetis derived from an empirical network study of the French field of cancer research. The data includes and combines inter‐laboratory networks, inter‐individualnetworks within the “elite” of French cancer researchers in that system, and performance variations (impact factor scores associated to these researchers’publications) measured at the individual level.