  Influence maximization (IM) has garnered a lot of attention in the literature
owing to applications such as viral marketing and infection containment. It
aims to select a small number of seed users to adopt an item such that adoption
propagates to a large number of users in the network. Competitive IM focuses on
the propagation of competing items in the network. Existing works on
competitive IM have several limitations. (1) They fail to incorporate economic
incentives in users' decision making in item adoptions. (2) Majority of the
works aim to maximize the adoption of one particular item, and ignore the
collective role that different items play. (3) They focus mostly on one aspect
of competition -- pure competition. To address these concerns we study
competitive IM under a utility-driven propagation model called UIC, and study
social welfare maximization. The problem in general is not only NP-hard but
also NP-hard to approximate within any constant factor. We, therefore, devise
instant dependent efficient approximation algorithms for the general case as
well as a $(1-1/e-\epsilon)$-approximation algorithm for a restricted setting.
Our algorithms outperform different baselines on competitive IM, both in terms
of solution quality and running time on large real networks under both
synthetic and real utility configurations.
