  The concept of community detection has long been used as a key device for
handling the mesoscale structures in networks. Suitably conducted community
detection reveals various embedded informative substructures of network
topology. However, regarding the practical usage of community detection, it has
always been a tricky problem to assign a reasonable community resolution for
networks of interest. Because of the absence of the unanimously accepted
criterion, most of the previous studies utilized rather ad hoc heuristics to
decide the community resolution. In this work, we harness the concept of
consistency in community structures of networks to provide the overall
community resolution landscape of networks, which we eventually take to
quantify the reliability of detected communities for a given resolution
parameter. More precisely, we exploit the ambiguity in the results of
stochastic detection algorithms and suggest a method that denotes the relative
validity of community structures in regard to their stability of global and
local inconsistency measures using multiple detection processes. Applying our
framework to synthetic and real networks, we confirm that it effectively
displays insightful fundamental aspects of community structures.
