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

Embeddedness In Affiliation NetworksTutzauer, Frank2‐Mode NetworksStatistical Models, Embeddedness, Two‐mode Networks, Affiliation Networks, HypergraphsSAT.AM1An affiliation network consists of actors and events. Actors are affiliated with each other by virtue of the events they mutually attend. Every affiliationnetwork has a representation as a bipartite graph, where an edge is placed between an actor and an event only if the actor attended that event, and also as ahypergraph, where the events correspond to the vertices of the hypergraph and actors are defined by subsets of vertices, i.e. by the events they attend.By examining these subsets, we distinguish between differing levels of embeddedness in the network. At one extreme, we might have an actor who attendedmany events, but none of these events were attended by any of the other actors in the network. Such an actor would show up in the hypergraph as a subsethaving an empty intersection with each of the other subsets. Even though this actor might be of high degree (in the bipartite graph), in no reasonableinterpretation would this actor be considered highly embedded in the affiliation network, at least not in the sense of having shared events with other actors.At the other extreme, we might have an actor defined by a collection of events, all of which were attended by another actor(s). In the hypergraph, the firstactor would be a subset of the other actor(s), and we would feel justified in claiming that this actor is as embedded in the network as one possibly can be.Most actors will be between these extremes, with some events being shared by varying others, and some not. In this paper, we introduce an embeddednessmeasure based on the cardinality of the largest set‐theoretic intersection between the actor under consideration and all other actors in the network, and weshow its cumulative distribution function to be conveniently expressed as a difference of binomials.Emerging Networks Of Online Social SupportWalker, Kasey L.; Mills, Carol B.Qualitative and Mixed Method Network studiesSocial Support, Online NetworksSAT.PM1Networks of social support are a ubiquitous feature of positive human interaction. Findings have consistently shown that individuals with adequate socialsupport tend to have better relationships and less stress, and tend to be more physically fit and live longer than individuals with low levels of support. Socialsupport, however, is no longer solely the province of face‐to‐face relationships; online social support can be an integral part of support seeking and provision.While there has been a great deal of research concerning online social support, some fundamental questions remain concerning its content and structure. Thisresearch seeks to answer those questions through the use of both content analysis and social network analysis. We followed one online depression‐supportgroup for one month. During this month there were 104 streams with a total of 631 coded posts. Initial results contradict assumptions about face to facesupport that place a high premium on emotional support and long‐lasting, densely connected networks. Our findings indicate that informational support is themost common form of support, even given in response to requests for emotional support. Furthermore, the networks are brief and sparsely connected. Themanuscript then discusses the implications of these findings for understanding both face to face and online social support.