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 inter...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.展开更多
文摘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.
