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

Reasoning About Large‐scale Social Networks With Probabilistic LogicGrgic, Sinisa; Lauc, DavorEmpirical Large‐N NetworksLink Prediction, Large‐scale Networks, Algorithms, Data Mining, Node Identification, Network MatchingSUN.AM2Network Matching and Link Prediction are relatively unexplored in the area of Social network analysis, but solving those problems in an efficient way is crucialin many real‐world applications. Network Matching is a generalized problem of node identification. Node identification (matching individuals) is a task ofunique identification a person in the analyzed network as a known entity in existing network, based on the known links and additional attributes. In the LinkPrediction (social) graph is build or completed by inferencing links based on existing network's structure and node attributes. Both problems in the most realworldapplications have to deal with incomplete information and probabilities. In perfect information environment, those problems would be naturallymodelled in the predicate logic, hence, the real‐world problems require methods of probabilistic logic ("ProbLog" framework). Two large‐scale social networkswere used to develop and test the model: (1) the sample of the largest social network consisting of 372 volunteers with over 1M links; (2) huge social networkgenerated from all available Croatian public records with 540.000 individuals and over 100 million links among them. First network was matched with thesecond using developed model, with completeness of 86,8% (323 individuals). Results are evaluated against matched volunteers with an error of 5,3%.Probabalistic logic link prediction model was applied on a second network with promising results.Reform At The Edge Of Chaos: Connecting Complexity, Social Networks, And Policy ImplementationDaly, Alan J.; Moolenaar, Nienke M.Leadership NetworksImplementation, Organizational Change, Leadership, Longitudinal AnalysisSAT.PM1Reforming public education is often a refrain in political discourse. Pronouncements of the failure of the educational systems often lead to reform policiesmeant to improve schooling. Once in place the assumption is that these policies will be implemented with fidelity and result in the intended outcomes.However, what is often lacking is careful consideration or examination of how policies are implemented by actors. Drawing on complexity theory, this paperargues that common rational assumptions undergirding current reform policies (such as linearity and uniformity) limit our understanding of how policy isenacted through complex social interactions. In this paper we examine the interactions among leaders across a school district over time as they implementreform. Our aim is to better understand how policy implementation evolves through social interaction. In a three‐year study, we examined theimplementation of a reform policy targeted at improving a consistently underperforming school district under progressive sanction. We collected data at threetime points and utilized longitudinal social network modeling (SIENA) to illustrate how districts can be conceptualized as complex adaptive systems. Resultssuggest a distinct complex pattern of social interactions underlying the process of policy implementation with district and site leaders tending to formrelationships based on reciprocity, triadic closure, similarity, popularity, and administrative experience.