Effective Influence Maximization with Priority

Jinghao Wang, Yanping Wu, Xiaoyang Wang, Chen Chen, Ying Zhang, Lu Qin

Track: Social networks and social media

Keywords: Influence Maximization, Approximation Algorithms, Adaptive Submodularity

Abstract: Influence maximization (IM) aims to identify a small set of influential users to maximize the information spread. It has been widely applied in the context of viral marketing, where a company distributes incentives to a few influencers to promote the product. However, in practical scenarios, not all users hold equal importance and certain users need to be prioritized for the specific requirements. Motivated by this, recently, a variant problem of IM, called influence maximization with priority (IMP), has been proposed. Given a graph G=(V,E), a priority set P ⊆ V and a threshold T ∈ [0, |P|], IMP aims to identify a set of k nodes (termed seeds) to maximize the expected number of activated nodes in G while satisfying that the expected number of activated nodes in P is no less than the given threshold. Nevertheless, we show that existing solutions for IMP are inferior in maximizing the influence spread in G, and can only offer poor approximation ratios in many cases. To address these limitations, in this paper, we first propose a novel framework named SAR with both superior empirical effectiveness and strong theoretical guarantees. In addition, to obtain more practical results, we study the IMP problem under the adaptive setting, where the seed users are iteratively selected after observing the diffusion result of the previous seeds. We design a scalable and effective algorithm AAS that achieves expected approximation guarantees. Comprehensive experiments on 5 real-world datasets are conducted to validate the performance of the proposed techniques. Compared with the state-of-the-art method, SAR achieves up to 22.3% larger spread and AAS achieves up to 42.6% larger spread, with both exhibiting a higher empirical approximation ratio.

Submission Number: 597