Võrdle meetodeid

	Võrguökonomeetria (eakaaslaste mõju)	Tsentraalsuse analüüs
Valdkond	Ökonomeetria	Võrgustikuanalüüs
Perekond	Regression model	Process / pipeline
Tekkeaasta	2009	1979
Looja	Yann Bramoullé, Habiba Djebbari & Bernard Fortin	Linton C. Freeman
Tüüp	Linear-in-means peer effects regression	Descriptive / exploratory network measure family
Algallikas	Bramoullé, Y., Djebbari, H., & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics, 150(1), 41–55.	Freeman, L.C. (1979). Centrality in Social Networks: Conceptual Clarification. Social Networks, 1(3), 215-239.
Rööpnimetused	Social Interactions Model, Peer Effects Model, Social Network Regression, Ağ Ekonometrisi	Merkeziyet Analizi (Degree, Betweenness, Eigenvector), node centrality, centrality measures, graph centrality
Seotud	3	5
Kokkuvõte	Network econometrics estimates how individuals' outcomes are causally shaped by the behaviour and characteristics of their social-network neighbours. Formalised by Bramoullé, Djebbari, and Fortin (2009), the framework embeds a row-normalised adjacency matrix into a linear regression, separating endogenous peer effects (imitation of outcomes), exogenous contextual effects (influence of neighbours' attributes), and correlated effects (shared environment), while using network topology to construct valid instruments.	Centrality analysis is a family of network-analytic measures, formalized by Freeman (1979), that quantifies the structural importance of individual nodes within a graph. Each centrality index captures a distinct mechanism of influence: degree centrality reflects direct connectivity, betweenness centrality identifies nodes that broker information flow, closeness centrality captures proximity to all others, and eigenvector centrality (along with PageRank) rewards connection to highly connected neighbors.