  Traditional viral marketing problems aim at selecting a subset of seed users
for one single product to maximize its awareness in social networks. However,
in real scenarios, multiple products can be promoted in social networks at the
same time. At the product level, the relationships among these products can be
quite intertwined, e.g., competing, complementary and independent. In this
paper, we will study the "interTwined Influence Maximization" (i.e., TIM)
problem for one product that we target on in online social networks, where
multiple other competing/complementary/independent products are being promoted
simultaneously. The TIM problem is very challenging to solve due to (1) few
existing models can handle the intertwined diffusion procedure of multiple
products concurrently, and (2) optimal seed user selection for the target
product may depend on other products' marketing strategies a lot. To address
the TIM problem, a unified greedy framework TIER (interTwined Influence
EstimatoR) is proposed in this paper. Extensive experiments conducted on four
different types of real-world social networks demonstrate that TIER can
outperform all the comparison methods with significant advantages in solving
the TIM problem.
