  Systems with two types of agents with a preference for heterophilous
interaction produces networks that are more or less close to bipartite. We
propose two measures quantifying the notion of bipartivity. The two
measures--one well-known and natural, but computationally intractable; one
computationally less complex, but also less intuitive--are examined on model
networks that continuously interpolates between bipartite graphs and graphs
with many odd circuits. We find that the bipartivity measures increase as we
tune the control parameters of the test networks to intuitively increase the
bipartivity, and thus conclude that the measures are quite relevant. We also
measure and discuss the values of our bipartivity measures for empirical social
networks (constructed from professional collaborations, Internet communities
and field surveys). Here we find, as expected, that networks arising from
romantic online interaction have high, and professional collaboration networks
have low bipartivity values. In some other cases, probably due to low average
degree of the network, the bipartivity measures cannot distinguish between
romantic and friendship oriented interaction.
