  Over the last decade, there has been an increasing interest in temporal
graphs, pushed by a growing availability of temporally-annotated network data
coming from social, biological and financial networks. Despite the importance
of analyzing complex temporal networks, there is a huge gap between the set of
definitions, algorithms and tools available to study large static graphs and
the ones available for temporal graphs. An important task in temporal graph
analysis is mining dense structures, i.e., identifying high-density subgraphs
together with the span in which this high density is observed. In this paper,
we introduce the concept of $(k, \Delta)$-truss (span-truss) in temporal
graphs, a temporal generalization of the $k$-truss, in which $k$ captures the
information about the density and $\Delta$ captures the time span in which this
density holds. We then propose novel and efficient algorithms to identify
maximal span-trusses, namely the ones not dominated by any other span-truss
neither in the order $k$ nor in the interval $\Delta$, and evaluate them on a
number of public available datasets.
