  Given a set ${\cal AL}$ of community detection algorithms and a graph $G$ as
inputs, we propose two ensemble methods $\mathtt{EnDisCO}$ and $\mathtt{MeDOC}$
that (respectively) identify disjoint and overlapping communities in $G$.
$\mathtt{EnDisCO}$ transforms a graph into a latent feature space by leveraging
multiple base solutions and discovers disjoint community structure.
$\mathtt{MeDOC}$ groups similar base communities into a meta-community and
detects both disjoint and overlapping community structures. Experiments are
conducted at different scales on both synthetically generated networks as well
as on several real-world networks for which the underlying ground-truth
community structure is available. Our extensive experiments show that both
algorithms outperform state-of-the-art non-ensemble algorithms by a significant
margin. Moreover, we compare $\mathtt{EnDisCO}$ and $\mathtt{MeDOC}$ with a
recent ensemble method for disjoint community detection and show that our
approaches achieve superior performance. To the best of our knowledge,
$\mathtt{MeDOC}$ is the first ensemble approach for overlapping community
detection.
