Topics in social network analysis and network science

This chapter introduces statistical methods used in the analysis of social networks and in the rapidly evolving parallel-field of network science. Although several instances of social network analysis in health services research have appeared recently, the majority involve only the most basic methods and thus scratch the surface of what might be accomplished. Cutting-edge methods using relevant examples and illustrations in health services research are provided

A LONGITUDINAL STATISTICAL NETWORK ANALYSIS OF THE ENVIRONMENTAL ITIGATION AND ALLIANCES IN THE UNITED STATES, 1970-2001

This dissertation investigates the structural dynamics of the inter-organizational (litigation, alliance) relations in the environmental movement sector (EMS) in the United States, 1970-2001. Particularly, it focuses on the litigative and alliance ties between the environmental organizations (EORGs) including both environmental movement organizations (EMOs) and environmental government agencies (EGAs), and explaining the processes by which the contemporary inter-EORG structure has emerged over time. The methods used in analysis include (balance, structural) partitioning, p-star logit, and categorical data analysis in statistical network analysis. The data analyzed were collected from various sources including LexisNexis and Guide Star and include both organizational attributes and relations. To explicate the dynamic processes by which the contemporary inter-EORG structure has emerged, this dissertation investigates the formation of dyadic, triadic, and network structure with regard to litigative and alliance ties, respectively. Selected fundamental models of network dynamics (transitive dominance, strategic actor, and social balance) help explain the empirical inter-organizational (litigation, alliance) relations in later chapters. The theoretical and empirical findings help better understand the structural and dynamic issues in the study of the environment, social movement, complex organizations, and network evolution

Reconstructing networks

Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. Given the extent of the subject, we shall focus on the inference methods rooted in statistical physics and information theory. The discussion will be organized according to the different scales of the reconstruction task, that is, whether the goal is to reconstruct the macroscopic structure of the network, to infer its mesoscale properties, or to predict the individual microscopic connections.Comment: 107 pages, 25 figure

Reconstructing networks

Complex networks datasets often come with the problem of missing information: interactions data that have not been measured or discovered, may be affected by errors, or are simply hidden because of privacy issues. This Element provides an overview of the ideas, methods and techniques to deal with this problem and that together define the field of network reconstruction. Given the extent of the subject, the authors focus on the inference methods rooted in statistical physics and information theory. The discussion is organized according to the different scales of the reconstruction task, that is, whether the goal is to reconstruct the macroscopic structure of the network, to infer its mesoscale properties, or to predict the individual microscopic connections

Probabilistic network analysis of social-ecological relationships emerging from EU LIFE projects for nature and biodiversity: An application of ERGM models in the case study of the Veneto region (Italy)

Considering social-ecological relationships in managing protected areas is fundamental to ensuring effective biodiversity conservation and restoration governance. Network analysis offers valuable methods to disentangle intangible relations between and within the social and ecological systems. In this way, it could be possible to identify and integrate multiple social and ecological variables that inevitably affect collaborative environmental governance's effectiveness. Nevertheless, this research area is still nascent, with few methodologies and concrete applications reported in the scientific literature. With this study, we aim to propose a robust novel application of a network methodology to enrich the evaluation of the effectiveness of collaborative environmental governance for nature and biodiversity, which has been applied through the analysis of social-ecological relationships that emerged from EU-cofounded LIFE-NAT projects. Specifically, we focus on LIFE-NAT projects implemented in the Veneto Region (Italy) financed in the last programming period (2014–2020). Through formulating four research hypotheses to be tested through Exponential Random Graph Models, we analyze 13 LIFE-NAT projects involving 83 social actors and 29 Natura 2000 (N2000) sites composed of 57 protected habitats. Results show that LIFE-NAT projects in Veneto Region stimulate polycentric governance. Nevertheless, they still need to concretize a multi-actor and multilevel governance. Furthermore, the analysis highlights that selected LIFE-NAT projects implement activities in N2000 sites able to support ecological connectivity and synergies across marine, freshwater, and land habitats through the bridging role of forests, especially in estuarine and coastal areas