  In this work, we extend a previous work where we proposed a suitable state
model built from a Karhunen-Loeve Transformation to build a new decision
process from which, we can extract useful knowledge and information about the
identified underlying sub-communities from an initial network. The aim of the
method is to build a framework for a multi-level knowledge retrieval. Besides
the capacity of the methodology to reduce the high dimensionality of the data,
the new detection scheme is able to extract, from the sub-communities, the
dense sub-groups with the definition and formulation of new quantities related
to the notions of energy and co-energy. The energy of a node is defined as the
rate of its participation to the set of activities while the notion of
co-energy defines the rate of interaction/link between two nodes. These two
important features are used to make each link weighted and bounded, so that we
are able to perform a thorough refinement of the sub-community discovery. This
study allows to perform a multi-level analysis by extracting information either
per-link or per-intra-subcommunity. As an improvement of this work, we define
the notion of pivot to relate the node(s) with the greatest influence in the
network. We propose the use of a thorough tool based on the formulation of the
transformation of a suitable probabilistic model into a possibilistic model to
extrac
