Efficient computational strategies for doubly intractable problems with applications to Bayesian social networks

Powerful ideas recently appeared in the literature are adjusted and combined to design improved samplers for Bayesian exponential random graph models. Different forms of adaptive Metropolis-Hastings proposals (vertical, horizontal and rectangular) are tested and combined with the Delayed rejection (DR) strategy with the aim of reducing the variance of the resulting Markov chain Monte Carlo estimators for a given computational time. In the examples treated in this paper the best combination, namely horizontal adaptation with delayed rejection, leads to a variance reduction that varies between 92% and 144% relative to the adaptive direction sampling approximate exchange algorithm of Caimo and Friel (2011). These results correspond to an increased performance which varies from 10% to 94% if we take simulation time into account. The highest improvements are obtained when highly correlated posterior distributions are considered.Comment: 23 pages, 8 figures. Accepted to appear in Statistics and Computin

Bayesian Exponential Random Graph Models with Nodal Random Effects

We extend the well-known and widely used Exponential Random Graph Model (ERGM) by including nodal random effects to compensate for heterogeneity in the nodes of a network. The Bayesian framework for ERGMs proposed by Caimo and Friel (2011) yields the basis of our modelling algorithm. A central question in network models is the question of model selection and following the Bayesian paradigm we focus on estimating Bayes factors. To do so we develop an approximate but feasible calculation of the Bayes factor which allows one to pursue model selection. Two data examples and a small simulation study illustrate our mixed model approach and the corresponding model selection.Comment: 23 pages, 9 figures, 3 table

Missing Data Augmentation for Bayesian Multiplex ERGMs

In this paper we present an estimation algorithm for Bayesian multiplex exponential random graphs (BmERGMs) under missing net- work data. Social actors are often connected with more than one type of relation, thus forming a multiplex network. It is important to consider these multiplex structures simultaneously when analyzing a multiplex network. The importance of proper models of multiplex network structures is even more pronounced under the issue of missing network data. The proposed algorithm is able to estimate BmERGMs under missing data and can be used to obtain proper multiple imputations for multiplex network structures. It is an extension of Bayesian exponential random graphs (BERGMs) as implemented in the Bergm package in R. We demonstrate the algorithm on a well known example, with and without artificially simulated missing data

Knowledge sharing in organizations: A Bayesian analysis of the role of reciprocity and formal structure

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordWe examine the conditions under which knowledge embedded in advice relations is likely to reach across intraorganizational boundaries and be shared between distant organizational members. We emphasize boundary-crossing relations because activities of knowledge transfer and sharing across subunit boundaries are systematically related to desirable organizational outcomes. Our main objective is to understand how organizational and social processes interact to sustain the transfer of knowledge carried by advice relations. Using original fieldwork and data that we have collected on members of the top management team in a multiunit industrial group, we show that knowledge embedded in task advice relations is unlikely to crosscut intraorganizational boundaries, unless advice relations are reciprocated, and supported by the presence of hierarchical relations linking managers in different subunits. The results we report are based on a novel Bayesian Exponential Random Graph Models (BERGMs) framework that allows us to test and assess the empirical value of our hypotheses while at the same time accounting for structural characteristics of the intraorganizational network of advice relations. We rely on computational and simulation methods to establish the consistency of the network implied by the model we propose with the structure of the intraorganizational network that we actually observed

Simultaneous modeling of initial conditions and time heterogeneity in dynamic networks: An application to Foreign Direct Investments

In dynamic networks, the presence of ties are subject both to endogenous network dependencies and spatial dependencies. Current statistical models for change over time are typically defined relative to some initial condition, thus skirting the issue of where the first network came from. Additionally, while these longitudinal network models may explain the dynamics of change in the network over time, they do not explain the change in those dynamics. We propose an extension to the longitudinal exponential random graph model that allows for simultaneous inference of the changes over time and the initial conditions, as well as relaxing assumptions of time-homogeneity. Estimation draws on recent Bayesian approaches for cross-sectional exponential random graph models and Bayesian hierarchical models. This is developed in the context of foreign direct investment relations in the global electricity industry in 1995–2003. International investment relations are known to be affected by factors related to: (i) the initial conditions determined by the geographical locations; (ii) time-dependent fluctuations in the global intensity of investment flows; and (iii) endogenous network dependencies. We rely on the well-known gravity model used in research on international trade to represent how spatial embedding and endogenous network dependencies jointly shape the dynamics of investment relations

Collaborations in environmental initiatives for an effective gover- nance of social-ecological systems: What the scientific literature suggests.

Moving from the scientific literature on evaluation of environmental projects and programs, this study identifies how and under which conditions collaborations are considered effective for adaptive gover- nance of SES. The method adopted is a systematic literature review based on the quantitative and qualitative analysis of 56 articles selected through specific queries on the SCOPUS database and published from 2004 to 2020. Results of the quantitative analysis underline conditions able to make collaborations effective for adaptive governance of SES: the importance of transdisciplinary research tackling both environmental and social sciences, the perceived urgency of stakeholders to tackle environmental challenges and consequently their inclusion in projects, the valorisation of different typologies of knowledge, and the adaptation to local culture and lifestyle. Results of the qualitative analysis provides specific recommendations for collaborations to be effective related to communication, equity, foresight, and respect, which need to be further strengthened. Multiplicity in visions and approaches should not be seen as a limit but as a resource able to stimulate creativity in social arrangements and environmental practices, making collaborations instrumental for the effectiveness of adaptive governance

Italian sociologists: A community of disconnected groups

Examining coauthorship networks is key to study scientific collaboration patterns and structural characteristics of scientific communities. Here, we studied coauthorship networks of sociologists in Italy, using temporal and multi-level quantitative analysis. By looking at publications indexed in Scopus, we detected research communities among Italian sociologists. We found that Italian sociologists are fractured in many disconnected groups. The giant connected component of the Italian sociology could be split into five main groups with a mixture of three main disciplinary topics: sociology of culture and communication (present in two groups), economic sociology (present in three groups) and general sociology (present in three groups). By applying an exponential random graph model, we found that collaboration ties are mainly driven by the research interests of these groups. Other factors, such as preferential attachment, gender and affiliation homophily are also important, but the effect of gender fades away once other factors are controlled for. Our research shows the advantages of multi-level and temporal network analysis in revealing the complexity of scientific collaboration patterns

Sustainable energy governance in South Tyrol (Italy): A probabilistic bipartite network model

At the national scale, almost all of the European countries have already achieved energy transition targets, while at the regional and local scales, there is still some potential to further push sustainable energy transitions. Regions and localities have the support of political, social, and economic actors who make decisions for meeting existing social, environmental and economic needs recognising local specificities. These actors compose the sustainable energy governance that is fundamental to effectively plan and manage energy resources. In collaborative relationships, these actors share, save, and protect several kinds of resources, thereby making energy transitions deeper and more effective. This research aimed to analyse a part of the sustainable energy governance composed of formal relationships between municipalities and public utilities and to investigate the opportunities to further spread sustainable energy development within a region. In the case study from South Tyrol, Italy, the network structures and dynamics of this part of the actual energy governance were investigated through a social network analysis and Bayesian exponential random graph models. The findings confirmed that almost all of the collaborations are based on spatial closeness relations and that the current network structures do not permit a further spread of the sustainable energy governance. The methodological approach can be replicated in other case studies and the findings are relevant to support energy planning choices at regional and local scales

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