Caimo, AlbertoAlbertoCaimoGollini, IsabellaIsabellaGollini2019-08-192019-08-192019 Elsev2020-02Computational Statistics & Data Analysis0167-9473http://hdl.handle.net/10197/10992A 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.enThis is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis (142, (2020)) https://doi.org/10.1016/j.csda.2019.106825Statistical network modelsWeighted networksBayesian analysisIntractable modelsJournal Article14211810.1016/j.csda.2019.1068252019-08-15https://creativecommons.org/licenses/by-nc-nd/3.0/ie/