  Community detection can be considered as a variant of cluster analysis
applied to complex networks. For this reason, all existing studies have been
using tools derived from this field when evaluating community detection
algorithms. However, those are not completely relevant in the context of
network analysis, because they ignore an essential part of the available
information: the network structure. Therefore, they can lead to incorrect
interpretations. In this article, we review these measures, and illustrate this
limitation. We propose a modification to solve this problem, and apply it to
the three most widespread measures: purity, Rand index and normalized mutual
information (NMI). We then perform an experimental evaluation on artificially
generated networks with realistic community structure. We assess the relevance
of the modified measures by comparison with their traditional counterparts, and
also relatively to the topological properties of the community structures. On
these data, the modified NMI turns out to provide the most relevant results.
