  Community detection is a well established method for studying the meso scale
structure of social networks. Applying a community detection algorithm results
in a division of a network into communities that is often used to inspect and
reason about community membership of specific nodes. This micro level
interpretation step of community structure is a crucial step in typical social
science research. However, the methodological caveat in this step is that
virtually all modern community detection methods are non-deterministic and
based on randomization and approximated results. This needs to be explicitly
taken into consideration when reasoning about community membership of
individual nodes. To do so, we propose a metric of community membership
consistency, that provides node-level insights in how reliable the placement of
that node into a community really is. In addition, it enables us to distinguish
the community core members of a community. The usefulness of the proposed
metrics is demonstrated on corporate board interlock networks, in which
weighted links represent shared senior level directors between firms. Results
suggest that the community structure of global business groups is centered
around persistent communities consisting of core countries tied by geographical
and cultural proximity. In addition, we identify fringe countries that appear
to associate with a number of different global business communities.
