Abstract
The influence maximization problem asks to find a small number of early adopters of a product in a social network, such that the expected number of total adoptions is maximized over the network. The problem has been well-studied, but most of the studies focus on the case of a single product or purely competitive products. This paper proposes a new influence diffusion model for multiple complementary products, namely, the comparative linear threshold (Com-LT) model. Under the Com-LT model, we model the complementary relation by reducing the thresholds of nodes. With this model, we study two problems: SelfInfMax and CompInfMax. We prove that these two problems are both NP-Hard under the Com-LT model. For both the SelfInfMax and the CompInfMax problem, we theoretically analyze the monotonicity and submodularity, and accordingly leverage lower bound optimization to devise non-trivial effective approximation algorithms. We conduct experiments over 4 real-world datasets. The experimental results demonstrate the correctness and efficiency of the proposed algorithms.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.