Inferential Network Analysis

Cranmer, Skyler J.; Desmarais, Bruce A.; Morgan, Jason W.

doi:10.1017/9781316662915

Cited by 27 publications

(25 citation statements)

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“…Another competing explanation is that our networks are effects rather than causes of the other variables in the models, a phenomenon called "endogenous network formation." Techniques exist to model network formation (Hays et al 2010 in spatial econometrics;Cranmer et al 2021 in inferential network analysis) but we have not availed ourselves of them. However, we think it safe to treat neighbors as an exogenous network.…”

Section: Methodological Considerationsmentioning

confidence: 99%

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International Influence: The Hidden Dimension

Coppedge

Denison²,

Friesen³

et al. 2021

SSRN Journal

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Section: Methodological Considerationsmentioning

confidence: 99%

“…In this regard, our approach differs fundamentally from the fast-growing inferential network analysis approach, which uses exponential random graph models (ERGMs) primarily to explain network structures(Cranmer et al 2021). We take the networks as theoretically given and focus on estimating their effects on outcomes.…”

mentioning

confidence: 99%

International Influence: The Hidden Dimension

Coppedge

Denison²,

Friesen³

et al. 2021

SSRN Journal

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“…Rather, there are different situations in which one approach may be preferred to others. Dyadic Logit models have been applied to topics such as the study of international conflict and alliances, though these models have been the target of extensive criticism (see Cranmer et al, 2021). The substance of the criticism is that dyads are often unlikely to be independent on one another, as is assumed by the Logit model.…”

Section: The Analysis Of Bipartite Networkmentioning

confidence: 99%

Tweeting #RamNavami: A Comparison of Approaches to Analyzing Bipartite Networks

Heaney

2021

IIM Kozhikode Society & Management Review

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Bipartite networks, also known as two-mode networks or affiliation networks, are a class of networks in which actors or objects are partitioned into two sets, with interactions taking place across but not within sets. These networks are omnipresent in society, encompassing phenomena such as student-teacher interactions, coalition structures and international treaty participation. With growing data availability and proliferation in statistical estimators and software, scholars have increasingly sought to understand the methods available to model the data-generating processes in these networks. This article compares three methods for doing so: (a) Logit (b) the bipartite Exponential Random Graph Model (ERGM) and (c) the Relational Event Model (REM). This comparison demonstrates the relevance of choices with respect to dependence structures, temporality, parameter specification and data structure. Considering the example of Ram Navami, a Hindu festival celebrating the birth of Lord Ram, the ego network of tweets using #RamNavami on 21April 2021 is examined. The results of the analysis illustrate that critical modelling choices make a difference in the estimated parameters and the conclusions to be drawn from them.

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“…In contrast, ERGMs analyze network ties rather than individual characteristics. They do not assume independence and can account for both endogenous factors that stem from the interaction of individuals in the network, such as the tendency of individuals to reciprocate ties and exogenous factors, such as age, that contribute to tie/edge formation in networks (Cranmer and Desmarais, 2011; Cranmer et al , 2020).…”

Section: Analytic Strategymentioning

confidence: 99%

Social and programmatic interactions in a therapeutic community for women: an exponential random graph model analysis

Cao

Liao

Warren

2020

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Purpose To analyze networks of social interactions between the residents of a therapeutic community (TC) for women and the way, in which such interactions predict the discussion of issues that arise in treatment. Design/methodology/approach In total, 50 residents of a corrections-based TC for women were surveyed on the peers with whom they socialized informally, shared meals, shared letters from home and discussed issues that arose in treatment over a 12 h period. The data were analyzed using exponential random graph models (ERGM). Findings Reciprocity occurred in all networks while transitivity (a tendency of two residents who are connected to both connect to a third peer) occurred in all networks measuring informal social interactions. When controlling for reciprocity and transitivity, residents avoided spending social time or sharing meals with the same peers. There was no evidence of homophily by race, age or years of education. Homophily by entrance time and case manager occurred in social time. Case manager homophily occurred in the discussion of treatment issues but disappeared when controlling for social time and sharing letters from home. Research limitations/implications Social networks in this TC arise from factors endogenous to the TC itself. It should be possible to determine the characteristics of optimal social networks in TCs. External validity is limited. Practical implications It should be possible to intervene to optimize the social networks of TC residents. Originality/value This is the first ERGM analysis of both informal and formal interactions in a TC.

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Inferential Network Analysis

Cited by 27 publications

References 0 publications

International Influence: The Hidden Dimension

International Influence: The Hidden Dimension

Tweeting #RamNavami: A Comparison of Approaches to Analyzing Bipartite Networks

Social and programmatic interactions in a therapeutic community for women: an exponential random graph model analysis

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