  Complex data in social and natural sciences find effective representation
through networks, wherein quantitative and categorical information can be
associated with nodes and connecting edges. The internal structure of networks
can be explored using unsupervised machine learning methods known as community
detection algorithms. The process of community detection is inherently subject
to uncertainty as algorithms utilize heuristic approaches and randomised
procedures to explore vast solution spaces, resulting in non-deterministic
outcomes and variability in detected communities across multiple runs.
Moreover, many algorithms are not designed to identify outliers and may fail to
take into account that a network is an unordered mathematical entity. The main
aim of our work is to address these issues through a consensus-based approach
by introducing a new framework called Consensus Community Detection (CCD). Our
method can be applied to different community detection algorithms, allowing the
quantification of uncertainty for the whole network as well as for each node,
and providing three strategies for dealing with outliers: incorporate,
highlight, or group. The effectiveness of our approach is evaluated on
artificial benchmark networks.
