  Selecting a connected subnetwork enriched in individually important vertices
is an approach commonly used in many areas of bioinformatics, including
analysis of gene expression data, mutations, metabolomic profiles and others.
It can be formulated as a recovery of an active module from which an
experimental signal is generated. Commonly, methods for solving this problem
result in a single subnetwork that is considered to be a good candidate.
However, it is usually useful to consider not one but multiple candidate
modules at different significance threshold levels. Therefore, in this paper we
suggest to consider a problem of finding a vertex ranking instead of finding a
single module. We also propose two algorithms for solving this problem: one
that we consider to be optimal but computationally expensive for real-world
networks and one that works close to the optimal in practice and is also able
to work with big networks.
