  The representation of data and its relationships using networks is prevalent
in many research fields such as computational biology, medical informatics and
social networks. Recently, complex networks models have been introduced to
better capture the insights of the modelled scenarios. Among others, dual
networks -based models have been introduced, which consist in mapping
information as pair of networks containing the same nodes but different edges.
  We focus on the use of a novel approach to visualise and analyse dual
networks. The method uses two algorithms for community discovery, and it is
provided as a Python-based tool with a graphical user interface. The tool is
able to load dual networks and to extract both the densest connected subgraph
as well as the common modular communities. The latter is obtained by using an
adapted implementation of the Louvain algorithm.
  The proposed algorithm and graphical tool have been tested by using social,
biological, and co-authorship networks. Results demonstrate that the proposed
approach is efficient and is able to extract meaningful information from dual
networks. Finally, as contribution, the proposed graphical user interface can
be considered a valuable innovation to the context.
