  The classic influence maximization problem finds a limited number of
influential seed users in a social network such that the expected number of
influenced users in the network, following an influence cascade model, is
maximized. The problem has been studied in different settings, with further
generalization of the graph structure, e.g., edge weights and polarities,
target user categories, etc. In this paper, we introduce a unique influence
diffusion scenario involving a population that split into two distinct groups,
with opposing views. We aim at finding the top-$k$ influential seed nodes so to
simultaneously maximize the adoption of two distinct, antithetical opinions in
the two groups, respectively. Efficiently finding such influential users is
essential in a wide range of applications such as increasing voter engagement
and turnout, steering public debates and discussions on societal issues with
contentious opinions. We formulate this novel problem with the voter model to
simulate opinion diffusion and dynamics, and then design a linear-time and
exact algorithm COSiNeMax, while also investigating the long-term opinion
characteristics in the network. Our experiments with several real-world
datasets demonstrate the effectiveness and efficiency of the proposed
algorithm, compared to various baselines.
