  Community detection is a key task to further understand the function and the
structure of complex networks. Therefore, a strategy used to assess this task
must be able to avoid biased and incorrect results that might invalidate
further analyses or applications that rely on such communities. Two widely used
strategies to assess this task are generally known as structural and
functional. The structural strategy basically consists in detecting and
assessing such communities by using multiple methods and structural metrics. On
the other hand, the functional strategy might be used when ground truth data
are available to assess the detected communities. However, the evaluation of
communities based on such strategies is usually done in experimental
configurations that are largely susceptible to biases, a situation that is
inherent to algorithms, metrics and network data used in this task.
Furthermore, such strategies are not systematically combined in a way that
allows for the identification and mitigation of bias in the algorithms, metrics
or network data to converge into more consistent results. In this context, the
main contribution of this article is an approach that supports a robust quality
evaluation when detecting communities in real-world networks. In our approach,
we measure the quality of a community by applying the structural and functional
strategies, and the combination of both, to obtain different pieces of
evidence. Then, we consider the divergences and the consensus among the pieces
of evidence to identify and overcome possible sources of bias in community
detection algorithms, evaluation metrics, and network data. Experiments
conducted with several real and synthetic networks provided results that show
the effectiveness of our approach to obtain more consistent conclusions about
the quality of the detected communities.
