  The spread of influence in networks is a topic of great importance in many
application areas. For instance, one would like to maximise the coverage,
limiting the budget for marketing campaign initialisation and use the potential
of social influence. To tackle this and similar challenges, more than a decade
ago, researchers started to investigate the influence maximisation problem. The
challenge is to find the best set of initially activated seed nodes in order to
maximise the influence spread in networks. In typical approach we will activate
all seeds in single stage, at the beginning of the process, while in this work
we introduce and evaluate a new approach for seeds activation in temporal
networks based on sequential seeding. Instead of activating all nodes at the
same time, this method distributes the activations of seeds, leading to higher
ranges of influence spread. The results of experiments performed using real and
randomised networks demonstrate that the proposed method outperforms single
stage seeding in 71% of cases by nearly 6% on average. Knowing that temporal
networks are an adequate choice for modelling dynamic processes, the results of
this work can be interpreted as encouraging to apply temporal sequential
seeding for real world cases, especially knowing that more sophisticated seed
selection strategies can be implemented by using the seed activation strategy
introduced in this work.
