Two-mode networks are data structure in which relations are collected on two different sets
of actors (dyadic), or one set of actors and one set of events (affiliation). In some cases, the
level/strength of ties can be discrete, continuous or coded by a set of ordered categories.
Many analytical tools used to analyze one-mode network must have been adapted in order to
deal with such networks. Usually, when relationships are valued, the data are dichotomized
(often by adopting an arbitrary level of dichotomization) resulting in information loss. When
the interest consists in visualizing and graphically analyzing the relational structures, it is
possible to use weighted bipartite graphs, spring embedding and correspondence analysis
(CA).
In this work we will discuss how CA with doubling coding can be useful to analyze and
graphically represent valued two-mode networks. Doubling has been originally designed to
handle bipolar variables –ordinal variables or ratings– like those resulting from the detection
of level/strength of ties with rating scale.
In particular we will discuss how the proposed approach: i) takes into account the nature of
relational data and the asymmetry of the two sets of entities in two-mode networks; ii)
permits to directly analyze valued relational data, avoiding loss of information; iii) deals with
the nature of the ratings and their bipolar character; v) improves visualization readability and
results interpretation.
In a nutshell, the proposed method allows to suitably represent the underlying weighted
relational distance among actors and events. Moreover, the positions of actors and events in
their respective factorial spaces have a nice relational interpretation, depending on the
level/strength of the observed ties.
We present the proposed approach by analyzing a subset of the relational data on the 1980
monetary donations from corporations to non-profit organizations in the Minneapolis-St.Paul
area.
