A multilayer exponential random graph modelling approach for weighted networks
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|Title:||A multilayer exponential random graph modelling approach for weighted networks||Authors:||Caimo, Alberto; Gollini, Isabella||Permanent link:||http://hdl.handle.net/10197/10992||Date:||14-Aug-2019||Online since:||2019-08-19T10:21:23Z||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.||Type of material:||Journal Article||Publisher:||Elsevier BV||Journal:||Computational Statistics & Data Analysis||Volume:||142||Copyright (published version):||2019 Elsevier||Keywords:||Statistical network models; Weighted networks; Bayesian analysis; Intractable models||DOI:||10.1016/j.csda.2019.106825||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mathematics and Statistics Research Collection|
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