  The use of network based approaches to model and analyse large datasets is
currently a growing research field. For instance in biology and medicine,
networks are used to model interactions among biological molecules as well as
relations among patients. Similarly, data coming from social networks can be
trivially modelled by using graphs. More recently, the use of dual networks
gained the attention of researchers. A dual network model uses a pair of graphs
to model a scenario in which one of the two graphs is usually unweighted (a
network representing physical associations among nodes) while the other one is
edge-weighted (a network representing conceptual associations among nodes). In
this paper we focus on the problem of finding the Densest Connected sub-graph
(DCS) having the largest density in the conceptual network which is also
connected in the physical network. The problem is relevant but also
computationally hard, therefore the need for introducing of novel algorithms
arises. We formalise the problem and then we map DCS into a graph alignment
problem. Then we propose a possible solution. A set of experiments is also
presented to support our approach.
