In social networks, nodes are organized into
densely linked communities where edges appear among the
nodes with high concentration. Identifying communities has
proven to be a challenging task due to various community
definitions/algorithms and also due to the lack of “ground
truth” for reference and evaluation. These communities not
only differ due to various definitions but also can be affected
by the type of interactions modeled in the network, which lead
to different social groups. We are interested in exploring and
studying the concept of partial network views, which is based
on multiple types of interactions. An Enron email network is
used to conduct our experiments. In this paper, we explore
the mutual impact of selecting different views extracted from
the same network and their interplay with various community
detection algorithms to measure the change and the level of
realism of the structure for non-overlapping communities. To
better understand this, we assess the agreement of partitions by
evaluating the partitioning quality (performance) and finding
the similarity between algorithms. The results demonstrate that
the topological properties of communities and the performance
of algorithms are equivalent to each other. Both of them are
affected by the type of interaction specified in each view. Some
network views appeared to have more interesting communities
than other views, thus, might help to approach a relatively
informative and logic “ground truth” for communities
