Recovering social networks from individual attributes

Arnold Polanski, Duncan McVicar

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Pages (from-to)287-311
Number of pages25
JournalJournal of Mathematical Sociology
Issue number4
Publication statusPublished - 2011

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