A multilayer exponential random graph modelling approach for weighted networks
Alberto Caimo and
Isabella Gollini
Computational Statistics & Data Analysis, 2020, vol. 142, issue C
Abstract:
A new modelling approach for the analysis of weighted networks with ordinal/ polytomous dyadic values is introduced. Specifically, it is proposed to model the weighted network connectivity structure using a hierarchical multilayer exponential random graph model (ERGM) generative process where each network layer represents a different ordinal dyadic category. The network layers are assumed to be generated by an ERGM process conditional on their closest lower network layers. A crucial advantage of the proposed method is the possibility of adopting the binary network statistics specification to describe both the between-layer and across-layer network processes and thus facilitating the interpretation of the parameter estimates associated to the network effects included in the model. The Bayesian approach provides a natural way to quantify the uncertainty associated to the model parameters. From a computational point of view, an extension of the approximate exchange algorithm is proposed to sample from the doubly-intractable parameter posterior distribution. A simulation study is carried out on artificial data and applications of the methodology are illustrated on well-known datasets. Finally, a goodness-of-fit diagnostic procedure for model assessment is proposed.
Keywords: Statistical network models; Weighted networks; Bayesian analysis; Intractable models (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:142:y:2020:i:c:s0167947319301720
DOI: 10.1016/j.csda.2019.106825
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