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.
|Number of pages||25|
|Journal||Journal of Mathematical Sociology|
|Publication status||Published - Oct 2011|
ASJC Scopus subject areas
- Algebra and Number Theory
- Social Sciences (miscellaneous)
- Sociology and Political Science