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Sampling bias in systems with structural heterogeneity and limited internal diffusion

Abstract:

Complex-systems research is becomingly increasingly data-driven, particularly in the social and biological domains. Many of the systems from which sample data are collected feature structural heterogeneity at the mesoscopic scale (i.e. communities) and limited inter-community diffusion. Here we show that the interplay between these two features can yield a significant bias in the global characteristics inferred from the data. We present a general framework to quantify this bias, and derive an...

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Authors


Jukka-Pekka Onnela More by this author
Neil F. Johnson More by this author
Sean Gourley More by this author
Gesine Reinert More by this author
Michael Spagat More by this author
Publication date:
2009
URN:
uuid:c81b7b36-f820-461a-be89-2b8e2e6dae8b
Local pid:
oai:eureka.sbs.ox.ac.uk:243

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