Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generate Country-Scale Networks of Interaction from Scattered Statistics   It is common to define the structure of interactions among a population of
agents by a network. Most of agent-based models were shown highly sensitive to
that network, so the relevance of simulation results directely depends on the
descriptive power of that network. When studying social dynamics in large
populations, that network cannot be collected, and is rather generated by
algorithms which aim to fit general properties of social networks. However,
more precise data is available at a country scale in the form of
socio-demographic studies, census or sociological studies. These "scattered
statistics" provide rich information, especially on agents' attributes, similar
properties of tied agents and affiliations. In this paper, we propose a generic
methodology to bring up together these scattered statistics with bayesian
networks. We explain how to generate a population of heterogeneous agents, and
how to create links by using both scattered statistics and knowledge on social
selection processes. The methodology is illustrated by generating an
interaction network for rural Kenya which includes familial structure,
colleagues and friendship constrained given field studies and statistics.

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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
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The categories of this paper are: hep-ex
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The categories of this paper are: eess
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The categories of this paper are: supr-con
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The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: econ
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph