  Influence maximization (IM) has been extensively studied for better viral
marketing. However, previous works put less emphasis on how balancedly the
audience are affected across different communities and how diversely the seed
nodes are selected. In this paper, we incorporate audience diversity and seed
diversity into the IM task. From the model perspective, in order to
characterize both influence spread and diversity in our objective function, we
adopt three commonly used utilities in economics (i.e., Perfect Substitutes,
Perfect Complements and Cobb-Douglas). We validate our choices of these three
functions by showing their nice properties. From the algorithmic perspective,
we present various approximation strategies to maximize the utilities. In
audience diversification, we propose a solution-dependent approximation
algorithm to circumvent the hardness results. In seed diversification, we prove
a ($1/e-\epsilon$) approximation ratio based on non-monotonic submodular
maximization. Experimental results show that our framework outperforms other
natural heuristics both in utility maximization and result diversification.
