  In recent years, community structure has emerged as a key component of
complex network analysis. As more data has been collected, researchers have
begun investigating changing community structure across multiple networks.
Several methods exist to analyze changing communities, but most of these are
limited to evolution of a single network over time. In addition, most of the
existing methods are more concerned with change at the community level than at
the level of the individual node. In this paper, we introduce scaled
inclusivity, which is a method to quantify the change in community structure
across networks. Scaled inclusivity evaluates the consistency of the
classiffication of every node in a network independently. In addition, the
method can be applied cross-sectionally as well as longitudinally. In this
paper, we calculate the scaled inclusivity for a set of simulated networks of
United States cities and a set of real networks consisting of teams that play
in the top division of American college football. We found that scaled
inclusivity yields reasonable results for the consistency of individual nodes
in both sets of networks. We propose that scaled inclusivity may provide a
useful way to quantify the change in a network's community structure.
