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• W2068846572 abstract "One of the most important challenges of network analysis remains the scarcity of reliable information on existing connection structures. This work explores theoretical and empirical methods of inferring directed networks from nodes attributes and from functions of these attributes that are computed for connected nodes. We discuss the conditions, under which an underlying connection structure can be (probabilistically) recovered, and propose a Bayesian recovery algorithm. In an empirical application, we test the algorithm on the data from the European School Survey Project on Alcohol and Other Drugs." @default.
• W2068846572 created "2016-06-24" @default.
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• W2068846572 creator A5073487138 @default.
• W2068846572 date "2011-10-01" @default.
• W2068846572 modified "2023-10-16" @default.
• W2068846572 title "Recovering Social Networks from Individual Attributes" @default.
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• W2068846572 doi "https://doi.org/10.1080/0022250x.2010.509525" @default.
• W2068846572 hasPublicationYear "2011" @default.
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