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- W2336436003 abstract "Abstract Exponential random graph models (ERGM) behave peculiar in large networks with thousand(s) of actors (nodes). Standard models containing 2-star or triangle counts as statistics are often unstable leading to completely full or empty networks. Moreover, numerical methods break down which makes it complicated to apply ERGMs to large networks. In this paper we propose two strategies to circumvent these obstacles. First, we use a subsampling scheme to obtain (conditionally) independent observations for model fitting and secondly, we show how linear statistics (like 2-stars etc.) can be replaced by smooth functional components. These two steps in combination allow to fit stable models to large network data, which is illustrated by a data example including a residual analysis." @default.
- W2336436003 created "2016-06-24" @default.
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- W2336436003 date "2017-05-01" @default.
- W2336436003 modified "2023-09-25" @default.
- W2336436003 title "Stable exponential random graph models with non-parametric components for large dense networks" @default.
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- W2336436003 doi "https://doi.org/10.1016/j.socnet.2016.12.002" @default.
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