  We study the effectiveness of using multiple phases for maximizing the extent
of information diffusion through a social network, and present insights while
considering various aspects. In particular, we focus on the independent cascade
model with the possibility of adaptively selecting seed nodes in multiple
phases based on the observed diffusion in preceding phases, and conduct a
detailed simulation study on real-world network datasets and various values of
seeding budgets. We first present a negative result that more phases do not
guarantee a better spread, however the adaptability advantage of more phases
generally leads to a better spread in practice, as observed on real-world
datasets. We study how diffusing in multiple phases affects the mean and
standard deviation of the distribution representing the extent of diffusion. We
then study how the number of phases impacts the effectiveness of multiphase
diffusion, how the diffusion progresses phase-by-phase, and what is an optimal
way to split the total seeding budget across phases. Our experiments suggest a
significant gain when we move from single phase to two phases, and an
appreciable gain when we further move to three phases, but the marginal gain
thereafter is usually not very significant. Our main conclusion is that, given
the number of phases, an optimal way to split the budget across phases is such
that the number of nodes influenced in each phase is almost the same.
