  Multilayer networks are the underlying structures of multiple real-world
systems where we have more than one type of interaction/relation between nodes:
social, biological, computer, or communication, to name only a few. In many
cases, they are helpful in modelling processes that happen on top of them,
which leads to gaining more knowledge about these phenomena. One example of
such a process is the spread of influence. Here, the members of a social system
spread the influence across the network by contacting each other, sharing
opinions or ideas, or - explicitly - by persuasion. Due to the importance of
this process, researchers investigate which members of a social network should
be chosen as initiators of influence spread to maximise the effect. In this
work, we follow this direction, develop and evaluate the sequential seeding
technique for multilayer networks. Until now, such techniques were evaluated
only using simple one layer networks. The results show that sequential seeding
in multilayer networks outperforms the traditional approach by increasing the
coverage and allowing to save the seeding budget. However, it also extends the
duration of the spreading process.
