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

Generosity As A Public Good: Heterogeneous Preferences In Partner Selection Promote Opinion Diversity And Social IntegrationSohn, YunkyuNetwork DynamicsPeer Influence, Co‐Evolution ModelTHURS.PM2Although polarization of public opinion and segregation of social groups are prevalent, we rarely observe complete fragmentation of a social network. Toexplain this regularity, we propose a utility‐based model of opinion formation and partner selection. The model allows feedback dynamics between opinionsand social relationships based on individual tolerance thresholds of opinion difference. Our model differs notably from the existing co‐evolutionary models inthe following aspects: i) Each dyadic relationship is weighted asymmetrically; ii) Opinion change is a Markov process; iii) In an equilibrium phase, where thedifference between opinions of agents holding a directed arc is lower than the tolerance threshold of the receiver, the resulting network and agents can havevarious topologies and opinion distributions. We investigate topological characteristics and opinion distribution of the model at equilibria when a) ahomogenous tolerance threshold is allowed and b) heterogeneous thresholds are distributed over the population. In contrast to the homogeneous thresholdcase where networks at equilibria exhibit either complete fragmentation by opinion or form fully connected components holding a homogenous opinion, theheterogeneous setting simultaneously achieves opinion diversity and social integration by binding structurally modularized opinion clusters with each other.The topological properties of these networks surprisingly resemble those of real social networks.Goodness Of Fit For Social Network DynamicsLospinoso, Joshua A.; Snijders, Tom A.Analyzing Network DataStatistical Methods, Dynamic Network Analysis, Siena, Goodness‐of‐fit, Actor‐based Models, DegeneracySAT.PM1We propose new statistical procedures for evaluating the goodness of fit of stochastic actor oriented models (SAOMs) for social network dynamics. Due to theunique nature of longitudinal social network data (a single observed trajectory), classic tests for goodness of fit are generally inappropriate. We develop (1) ageneral non‐parametric test based on auxiliary features of the network like triad census counts, behavioral profiles, geodesic distances, etc., which leverages asimulated cumulative density function (2) an information criteria for comparing among likelihood based models for social network dynamics based on pathintegration, and (3) a series of parametric tests for model selection among nested SAOMs based on the classic asymptotic tests for composite hypotheses. Wepresent a simulation studies to illustrate the effectiveness of these approaches, and apply them as a systematic examination to a real world dataset.