TITLE:
Subset Multiple Correspondence Analysis as a Tool for Visualizing Affiliation Networks
AUTHORS:
Achilles Dramalidis, Angelos Markos
KEYWORDS:
Affiliation Networks, Two-Mode Networks, Subset Correspondence Analysis
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.4 No.2,
May
19,
2016
ABSTRACT: In this paper we investigate the potential of Subset Multiple Correspondence Analysis (s-MCA), a
variant of MCA, to visually explore two-mode networks. We discuss how s-MCA can be useful to
focus the analysis on interesting subsets of events in an affiliation network while preserving the
properties of the analysis of the complete network. This unique characteristic of the method is also
particularly relevant to address the problem of missing data, where it can be used to partial out
their influence and reveal the more substantive relational patterns. Similar to ordinary MCA, s-
MCA can also alleviate the problem of overcrowded visualizations and can effectively identify associations
between observed relational patterns and exogenous variables. All of these properties
are illustrated on a student course-taking affiliation network.