Spatial networks describe relations among agents that live in a metric space and whose locations affect the probability of connections. Recently, nonpara- metric Bayesian statistics (BNP) proved itself to be a powerful tool to provide random graph models that mimic real world networks, but no proposals have been made so far to include spatial covariates. I will show how some available models fail in recovering spatial information and conjecture a way to solve the problem.
Issues with sparse spatial random graphs / Panero, Francesca. - (2023), pp. 250-253. (Intervento presentato al convegno CLADAG 2023 14-th Scientific Meeting Classification and Data Analysis Group tenutosi a Salerno; Italy).
Issues with sparse spatial random graphs
Panero, FrancescaPrimo
2023
Abstract
Spatial networks describe relations among agents that live in a metric space and whose locations affect the probability of connections. Recently, nonpara- metric Bayesian statistics (BNP) proved itself to be a powerful tool to provide random graph models that mimic real world networks, but no proposals have been made so far to include spatial covariates. I will show how some available models fail in recovering spatial information and conjecture a way to solve the problem.| File | Dimensione | Formato | |
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