  Recently, evolving networks are becoming a suitable form to model many
real-world complex systems, due to their peculiarities to represent the systems
and their constituting entities, the interactions between the entities and the
time-variability of their structure and properties. Designing computational
models able to analyze evolving networks becomes relevant in many applications.
The goal of this research project is to evaluate the possible contribution of
temporal pattern mining techniques in the analysis of evolving networks. In
particular, we aim at exploiting available snapshots for the recognition of
valuable and potentially useful knowledge about the temporal dynamics exhibited
by the network over the time, without making any prior assumption about the
underlying evolutionary schema. Pattern-based approaches of temporal pattern
mining can be exploited to detect and characterize changes exhibited by a
network over the time, starting from observed snapshots.
