  Motivation: Discovering functional modules in protein-protein interaction
(PPI) networks by optimization methods remains a longstanding challenge in
biology. Traditional algorithms simply consider strong protein complexes that
can be found in the original network by optimizing some metrics, which causes
obstacles for the discovery of weak and hidden complexes shielded by stronger
complexes. Also, protein complexes are not only in different density but also
in a large range of scale, making it extremely difficult to be detected. Toward
this objective, we propose a hierarchical hidden community approach to predict
protein complexes. Results: We propose a method called HirHide (Hierarchical
Hidden Community Detection), which can be combined with traditional community
detection methods to enable them to discover hierarchical hidden communities.
It is the first community detection algorithm that can find a hierarchical
structure as well as hidden structure. We compare the performance of three
traditional methods with their HirHide versions. Experimental results show that
the HirHide methods using traditional methods as the base algorithms achieve
better performance, sometimes even significantly outperform the baselines.
