  Many times the nodes of a complex network, whether deliberately or not, are
aggregated for technical, ethical, legal limitations or privacy reasons. A
common example is the geographic position: one may uncover communities in a
network of places, or of individuals identified with their typical geographical
position, and then aggregate these places into larger entities, such as
municipalities, thus obtaining another network. The communities found in the
networks obtained at various levels of aggregation may exhibit various degrees
of similarity, from full alignment to perfect independence. This is akin to the
problem of ecological and atomic fallacies in statistics, or to the Modified
Areal Unit Problem in geography. We identify the class of community detection
algorithms most suitable to cope with node aggregation, and develop an index
for aggregability, capturing to which extent the aggregation preserves the
community structure. We illustrate its relevance on real-world examples (mobile
phone and Twitter reply-to networks). Our main message is that any
node-partitioning analysis performed on aggregated networks should be
interpreted with caution, as the outcome may be strongly influenced by the
level of the aggregation.
