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

Model Selection Of Exponential‐family Social Network ModelsWang, Ranran; Handcock, Mark S.Exponential Random GraphsBayesian Methods, Exponential‐family Random Graph Models, Goodness‐of‐fit, Model selectionSUN.AM1Exponential‐family random graph (ERG) models have been widely applied in social network analysis. Hunter, Goodreau and Handcock (2006) developedprocedures for the goodness‐of‐fit of ERG models based on graphical diagnostics. However, the problems of model selection for ERG models have yet not beenwell studied. In this paper, we investigate model selection for ERG models using both likelihood ratio tests and Bayesian methods. We propose a novelsystematic procedure to conduct likelihood ratio tests to compare ERG models. Given two sets of models, we evaluate the likelihood ratio statistic, explore itssampling distribution and calculate the Monte‐Carlo p‐values at the end. Bayesian inference has been recently applied to ERG models to resolve modeldegeneracy and bias‐reduction problems. We develop a numerical algorithm to estimate the Bayes factor for given models. Finally, likelihood ratio tests andBayesian model selection are tested and compared using real social network data.Modeling Innovation Arms Races In Socio‐technical NetworksThomas, Russell C.; Metgher, CristinaPoster SessionMixed Methods, Innovation, Co‐evolution, Socio‐technical Network, agent‐based modeling, Information securitySAT.PM3Information security has been commonly viewed as a rivalry between attackers and defenders, and it has been popularly described as an evolutionary armsrace where each side has incentives to continually create new innovations to overcome the opponent’s capabilities. In doing so, neither side gains a lastingadvantage (i.e. the Red Queen effect). From a research viewpoint, there has been very little research on computational models of innovation and the coevolutionarydynamics of innovation in adversarial networks We explore these problems in the context of email spam and anti‐spam defenses using a hybridcomputational model designed to be ‘history‐friendly’. We examine these questions: 1. Does adversarial co‐evolution inevitably lead to a Red Queen armsrace? 2. How do incentive systems affect co‐evolution and does lack of incentives lead to underinvestment in innovation? We model the system at two levels.At the Innovation Process level, we have built a Multi‐Agent System (MAS) where the agents interact with design solutions to either improve existing solutionsincrementally (‘exploitation’) or attempt to invent new solutions from available components (‘exploration’). Agents also learn from each other throughcommunities of practice. At the Transaction level we will adapt the system dynamics model to model the sending and receiving of email and to observe thecost and volume dynamics of spam and anti‐spam technologies, systems, and business models.