Analyzing Farmers’ Network Structure Using ERGM: The Case of Ghana’s Cocoa Farmers

This study examines farmer’s network formation and structure based on their location. Through the use of ERGM, we analyzed a dataset of 200 Ghanaian cocoa farmers obtained from four different villages by the use of their social network information sharing. We explored the characteristics of these networks in the various locations to see their similarities or otherwise, reliance with the stakeholders in order to assess knowledge exchange, with the assumption that improvements in these variables will help to achieve high performance. We found out that even though the farmers considered were in the same region and they grow the same kind of crops, with about 90% of them from the same ethnic group, their network structure were significantly different. It is therefore recommended that, stakeholders need to study the structure of farmers’ network in their local locations before implementing important policies so as to get maximum productivity for their input. Thus, two networks may be for the same purpose, but might not have the same structure. Keywords: exponential random graph models; farmers’ network; cocoa sustainability; farmers’ social network; cocoa farmers; structural characteristics; network formation DOI: 10.7176/JRDM/78-04 Publication date:August 31st 202

Statistical Network Analysis with Bergm

Recent advances in computational methods for intractable models have made network data increasingly amenable to statistical analysis. Exponential random graph models (ERGMs) emerged as one of the main families of models capable of capturing the complex dependence structure of network data in a wide range of applied contexts. The Bergm package for R has become a popular package to carry out Bayesian parameter inference, missing data imputation, model selection and goodness-of-fit diagnostics for ERGMs. Over the last few years, the package has been considerably improved in terms of efficiency by adopting some of the state-of-the-art Bayesian computational methods for doubly-intractable distributions. Recently, version 5 of the package has been made available on CRAN having undergone a substantial makeover, which has made it more accessible and easy to use for practitioners. New functions include data augmentation procedures based on the approximate exchange algorithm for dealing with missing data, adjusted pseudo-likelihood and pseudo-posterior procedures, which allow for fast approximate inference of the ERGM parameter posterior and model evidence for networks on several thousands nodes.Comment: 22 pages, 5 figure

A multilayer exponential random graph modelling approach for weighted networks

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.24 month embargo - ACUpdate issue date during checkdate report - A