Goodness of Fit of Social Network Models

Hunter, David R.; Goodreau, Steven M.; Handcock, Mark S.

doi:10.1198/016214507000000446

Cited by 577 publications

(523 citation statements)

References 12 publications

(24 reference statements)

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“…A good model will generate a distribution of networks whose network statistics are not significantly different from those of the observed network (Robins et al 2007). Although a clear threshold has yet to be established in the literature, prominent applications (e.g., Hunter et al 2008b) implicitly use P = 0.05.…”

Section: Inferential Statistical Analysis Of Organizational Collaboramentioning

confidence: 99%

Discursive barriers and cross-scale forest governance in Central Kalimantan, Indonesia

Gallemore¹,

Dini²,

Prasti³

et al. 2014

E&S

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ABSTRACT. Students of social-ecological systems have emphasized the need for effective cross-scale governance. We theorized that discursive barriers, particularly between technical and traditional practices, can act as a barrier to cross-scale collaboration. We analyzed the effects of discursive divides on collaboration on Reducing Emissions from Deforestation and Forest Degradation (REDD+) policy development in Central Kalimantan, an Indonesian province on the island of Borneo selected in 2010 to pilot subnational REDD+ policy. We argue that the complexities of bridging local land management practices and technical approaches to greenhouse gas emissions reduction and carbon offsetting create barriers to cross-scale collaboration. We tested these hypotheses using an exponential random graph model of collaboration among 36 organizations active in REDD+ policy in the province. We found that discursive divides were associated with a decreased probability of collaboration between organizations and that organizations headquartered outside the province were less likely to collaborate with organizations headquartered in the province. We conclude that bridging discursive communities presents a chicken-and-egg problem for cross-scale governance of social-ecological systems. In precisely the situations where it is most important, when bridging transnational standards with local knowledge and land management practices, it is the most difficult.

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Section: Inferential Statistical Analysis Of Organizational Collaboramentioning

confidence: 99%

Discursive barriers and cross-scale forest governance in Central Kalimantan, Indonesia

Gallemore¹,

Dini²,

Prasti³

et al. 2014

E&S

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show abstract

“…Nevertheless, the edge and boundary effects this imposes on the analysis is acknowledged, as this is the case with any social network analysis whereby fixed boundaries are required to be enforced to aid meaningful study (Hunter et al, 2008).…”

Section: Datamentioning

confidence: 99%

Networks, Space and Organizational Performance: A Study of the Determinants of Industrial Research Income Generation by Universities

2015

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This paper examines the extent to which both network structure and spatial factors impact on the organisational performance of universities as measured by the generation of industrial research income. Drawing on data concerning the interactions of universities in the UK with large R&D-intensive firms, the paper employs both social network analysis and regression analysis. The analysis finds that the structural position of a university within networks with large R&D-intensive firms is significantly associated with the level of research income gained from industry. Spatial factors, on the other hand, are not found to be clearly associated with performance, suggesting that universities operate on a level playing field across regional environments once other factors are controlled for.

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“…ERG mod- els of historical import include, in addition to the simplest H(G) = θm(G), the Holland and Leinhardt model of reciprocity, the Strauss model of clustering, the 2-star model, and the generalized k-star models [5]. We refer interested readers to introductory articles and significant recent work from the SNA community for more detail [6][7][8]10].…”

Section: Introductionmentioning

confidence: 99%

“…See Ref. [10]) and, when a closer agreement is desired, refining the hypothesis and repeating the procedure. This process is presented schematically in Fig.…”

Section: Introductionmentioning

confidence: 99%

Linear and optimization Hamiltonians in clustered exponential random graph modeling

Park¹,

Yook²

2011

J. Stat. Mech.

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Exponential random graph theory is the complex network analog of the canonical ensemble theory from statistical physics. While it has been particularly successful in modeling networks with specified degree distributions, a naïve model of a clustered network using a graph Hamiltonian linear in the number of triangles has been shown to undergo an abrupt transition into an unrealistic phase of extreme clustering via triangle condensation. Here we study a non-linear graph Hamiltonian that explicitly forbids such a condensation and show numerically that it generates an equilibrium phase with specified intermediate clustering.

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Goodness of Fit of Social Network Models

Cited by 577 publications

References 12 publications

Discursive barriers and cross-scale forest governance in Central Kalimantan, Indonesia

Discursive barriers and cross-scale forest governance in Central Kalimantan, Indonesia

Networks, Space and Organizational Performance: A Study of the Determinants of Industrial Research Income Generation by Universities

Linear and optimization Hamiltonians in clustered exponential random graph modeling

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