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• W108395562 abstract "We discuss how the statistical notions of influential observations and outliersmay apply to the statistical models for social networks known as exponentialfamily random graph models (ERGMs or p-star). We argue that the mostfruitful approach is to consider observations on the level of the actors and thatif a removal of an actor would result in a radically different model, then itcould be said that the inferred characteristics of the network depend greatlyon this actor and hence the actor is in some sense ?central? with respect tothe structural features that ?matter?. To define how to ?remove? an actoris not straightforward because of the usually high degree of interdependenceamong tie-variables in ERGMs. We construe the removal in terms of two casedeletion strategies: the interaction (tie-variables) of an actor is assumed to beunobserved or, removal results in the subgraph induced by removing the corre-sponding vertex. We define the difference in inferred model resulting from casedeletion from the perspective of information theory and difference in estimates,both in the natural and mean value parametrisation, representing varying de-grees of approximation. We arrive at several measures of influence, three ofwhich do not require refitting of the model to the case deleted data and hencethese lend themselves to routine application in the ERGM fitting procedure.The influence measures are applied to a well known data set to illustrate theinformation they provide." @default.
• W108395562 created "2016-06-24" @default.
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• W108395562 date "2008-01-01" @default.
• W108395562 modified "2023-09-25" @default.
• W108395562 title "Extreme Actors - Outliers and Influential Observations in exponential random graph (p-star) models" @default.
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