  Cluster-based descriptions of biological networks have received much
attention in recent years fostered by accumulated evidence of the existence of
meaningful correlations between topological network clusters and biological
functional modules. Several well-performing clustering algorithms exist to
infer topological network partitions. However, due to respective technical
idiosyncrasies they might produce dissimilar modular decompositions of a given
network. In this contribution, we aimed to analyze how alternative modular
descriptions could condition the outcome of follow-up network biology analysis.
We considered a human protein interaction network and two paradigmatic cluster
recognition algorithms, namely: the Clauset-Newman-Moore and the infomap
procedures. We analyzed at what extent both methodologies yielded different
results in terms of granularity and biological congruency. In addition, taking
into account Guimera cartographic role characterization of network nodes, we
explored how the adoption of a given clustering methodology impinged on the
ability to highlight relevant network meso-scale connectivity patterns. As a
case study we considered a set of aging related proteins, and showed that only
the high-resolution modular description provided by infomap, could unveil
statistically significant associations between them and inter-intra modular
cartographic features. Besides reporting novel biological insights that could
be gained from the discovered associations, our contribution warns against
possible technical concerns that might affect the tools used to mine for
interaction patterns in network biology studies. In particular our results
suggested that sub-optimal partitions from the strict point of view of their
modularity levels might still be worth being analyzed when meso-scale features
were to be explored in connection with external source of biological knowledge.
