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

Hello Stranger – Reframing “familiar Strangers” In Light Of Location Based ServicesSchwartz, RazInnovation, Diffusion, and the Adoption of TechnologyInformation Technologies, Social NetworksTHURS.PM2We see them every day all around us. They are the “familiar strangers.” Coined by Stanley Milgram in the 1977, this term depicts a common socialphenomenon – a relationship between two complete strangers that recognize each other through their daily encounters in public places (such as the subway,gym etc.). Milgram claims that although these two people never communicate with each other, their relationship is real and it is based on both sides agreeingto mutually ignore each other. This study depicts the emergence of what I call the “virtual familiar stranger,” an update to the classic term, that both complieswith Milgram ideas but at the same time adds to it the influence of the virtual sphere brought by location based social services. These social networks that useGPS location data from mobile devices – such as Foursquare, Gowalla, Grindr, SCVNGR and Facebook Places – encourage users to check‐in to places they visit,leave tips, and see who else is around them while at the same time increase the number of familiar strangers in their surroundings. Through analyzinginterviews I conducted with several of the creators and users of these services, this study redefines Milgram's term of familiar stranger in light of locationbased social services and shows how the use of these services increase not only the number but also the significance of familiar strangers in daily life.HIV Transmission Among Men Who Have Sex With Men In The United States: New Insights From Dynamic Demographic Network ModelsGoodreau, Steven M.Social Networks and HealthHIV/STD, Sex Networks, Public Health, Exponential‐family Random Graph ModelsSAT.AM1I report on initial efforts to build a rich data‐driven model of HIV transmission among men who have sex with men (MSM) in the United States. The modelmakes numerous methodological developments of interest to network reserachers over earlier transmission models for MSM: (1) it relies on the ERGMframework for both behavioral model estimation and simulation; (2) it includes two kinds of relational networks: a dynamic one for steady partnerships (usingthe methods of Krivistky 2010), and a memoryless cross‐sectional network for casual contacts each day; and (3) it includes feedback between vital dynamicsand transmission on the one hand, and relational formation and dissolution on the other (also via the methods of Krivitsky (2010). The model incorporatesnumerous forms of demographic, relational, behavioral, and biological heterogeneity, parameterized from large‐scale surveys of MSM in the United States.Initial results suggest that 33% of infections occur within main partnerships, far less than the 68% estimated in a recent paper (Sullivan et al. 2009). Ourestimate for the proportion of infections originating with diagnosed, untreated men is high (59%). I conclude by discussing NIH's recent initiatives to investigatecombination interventions for HIV (of which this work is a part), and the resulting opportunity for network models like those presented here to play a majorrole in the coming years.