Multiple Correspondence Analysis and Multiple Factor Analysis have proved appropriate for visually analyzing affiliation (two-mode) networks. However, more could be said about the use of these tools within the positional approach of social network analysis, relying upon the ways in which both these factorial methods and blockmodeling can lead to an appraisal of positional equivalences. This paper presents a joint approach that combines all these methods in order to perform a positional analysis of time-varying affiliation networks. We present an application to an affiliation network of theatre companies involved in stage co-productions over four seasons. The study shows how the joint use of Multiple Factor Analysis and blockmodeling helps us understand network positions and the longitudinal affiliation patterns characterizing them.
On the analysis of time-varying affiliation networks: The case of stage co-productions / Ragozini, Giancarlo; Serino, Marco; D’Ambrosio, Daniela. - 227:(2018), pp. 119-129. (Intervento presentato al convegno 48th Scientific Meeting of the Italian Statistical Society, SIS 2016 tenutosi a ita nel 2016) [10.1007/978-3-319-73906-9_11].
On the analysis of time-varying affiliation networks: The case of stage co-productions
Ragozini, Giancarlo
;SERINO, MARCO;D’Ambrosio, Daniela
2018
Abstract
Multiple Correspondence Analysis and Multiple Factor Analysis have proved appropriate for visually analyzing affiliation (two-mode) networks. However, more could be said about the use of these tools within the positional approach of social network analysis, relying upon the ways in which both these factorial methods and blockmodeling can lead to an appraisal of positional equivalences. This paper presents a joint approach that combines all these methods in order to perform a positional analysis of time-varying affiliation networks. We present an application to an affiliation network of theatre companies involved in stage co-productions over four seasons. The study shows how the joint use of Multiple Factor Analysis and blockmodeling helps us understand network positions and the longitudinal affiliation patterns characterizing them.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.