Table 2.

Network metrics used to assess information-sharing network structure. Fishers (circles) and ties (arrows) outline the represented metric in the network. Red circles and arrows highlight the relevant structure of the network each metric measures. Black and white circles and black arrows highlight structures of the network that are not relevant to the metric measure in question.

metric	network structure	description	theoretical use in conservation-relevant systems	example
degree assortativity		a preference for individuals to associate with others that are similar in degree (e.g. high in-degree) [49,50]. Akin to degree homophily [51].	identifies individuals and pathways of individuals that could facilitate widespread diffusion of information about conservation initiatives in a community of conservation interest	a comprehensive, socio-centric network study of the Hadza hunter–gatherers of Tanzan was undertaken. Hadza networks were positively assorted by degree. People with higher in-degree named more social contacts, and people with higher out-degree were more likely to be named, even in models with controls. In other words, individuals who nominate more friends are more popular even among those they themselves did not nominate [52].
node eccentricity		the furthest network distance between an individual and all other individuals in the network [53]. Equivalent to the inverse of some definitions of ‘node closeness’.	can inform whether or not information relevant to a conservation initiative is shared in an even or clustered manner throughout a community on interest. This can inform how social norms and personal beliefs might affect information flow, which in turn can allow for conservation practitioners to tailor interventions to particular perspectives about a harmful activity (e.g. bycatch).	using social network analysis and several centrality measures including ‘node closeness’ (also equivalent to the inverse of some definitions of ‘node eccentricity’) the authors assess the structural nature and expanse of climate-based communication between professionals across sectors in the Pacific Islands region. Their results show a simultaneously diffuse and strongly connected network, with no isolated spatial or sectoral groups. The most central network members were shown to be those with a strong networking component to their professions [54].