  The problem of finding optimal set of users for influencing others in the
social network has been widely studied. Because it is NP-hard, some heuristics
were proposed to find sub-optimal solutions. Still, one of the commonly used
assumption is the one that seeds are chosen on the static network, not the
dynamic one. This static approach is in fact far from the real-world networks,
where new nodes may appear and old ones dynamically disappear in course of
time.
  The main purpose of this paper is to analyse how the results of one of the
typical models for spread of influence - linear threshold - differ depending on
the strategy of building the social network used later for choosing seeds. To
show the impact of network creation strategy on the final number of influenced
nodes - outcome of spread of influence, the results for three approaches were
studied: one static and two temporal with different granularities, i.e. various
number of time windows. Social networks for each time window encapsulated
dynamic changes in the network structure. Calculation of various node
structural measures like degree or betweenness respected these changes by means
of forgetting mechanism - more recent data had greater influence on node
measure values. These measures were, in turn, used for node ranking and their
selection for seeding.
  All concepts were applied to experimental verification on five real datasets.
The results revealed that temporal approach is always better than static and
the higher granularity in the temporal social network while seeding, the more
finally influenced nodes. Additionally, outdegree measure with exponential
forgetting typically outperformed other time-dependent structural measures, if
used for seed candidate ranking.
