Conditional Marginalization for Exponential Random Graph Models
نویسندگان
چکیده
For exponential random graph models, under quite general conditions, it is proved that induced subgraphs on node sets disconnected from the other nodes still have distributions from an exponential random graph model. This can help in the theoretical interpretation of such models. An application is that for saturated snowball samples from a potentially larger graph which is a realization of an exponential random graph model, it is possible to do the analysis of the observed snowball sample within the framework of exponential random graph models without any knowledge of the larger graph.
منابع مشابه
Statistical Models for Social Networks
SINGLE NETWORKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Conditionally Uniform Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Latent Space Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Exponential Random Graph Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Overview; Conditional In...
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