  The investigation of community structure in networks is a task of great
importance in many disciplines, namely physics, sociology, biology and computer
science where systems are often represented as graphs. One of the challenges is
to find local communities from a local viewpoint in a graph without global
information in order to reproduce the subjective hierarchical vision for each
vertex. In this paper we present the improvement of an information dynamics
algorithm in which the label propagation of nodes is based on the Markovian
flow of information in the network under cognitive-inspired constraints
\cite{Massaro2012}. In this framework we have introduced two more complex
heuristics that allow the algorithm to detect the multi-resolution hierarchical
community structure of networks from a source vertex or communities adopting
fixed values of model's parameters. Experimental results show that the proposed
methods are efficient and well-behaved in both real-world and synthetic
networks.
