RCELF: A residual-based approach for Influence Maximization Problem. Issue 102 (December 2021)

• Record Type: Journal Article
• Authors: Zhang, Shiqi; Zeng, Xinxun; Tang, Bo
• Is Part Of: Information systems. Issue 102(2021)
• Publication Date: 2021-12
• Subjects: Social network -- Influence maximization -- Database -- Applications; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2021.101828
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: Influence Maximization Problem (IMP ) is selecting a seed set of nodes in the social network to spread the influence as widely as possible. It has many applications in multiple domains, e.g., viral marketing is frequently used for new products or activities advertisement. While it is a classic and well-studied problem in computer science, unfortunately, all those proposed techniques are compromising among time efficiency, memory consumption, and result quality. In this paper, we conduct comprehensive experimental studies on the state-of-the-art IMP approximate approaches to reveal the underlying trade-off strategies. Interestingly, we find that even the state-of-the-art approaches are impractical when the propagation probability of the network have been taken into consideration. With the findings of existing approaches, we propose a novel residual-based approach (i.e., RCELF ) for IMP, which (i) overcomes the deficiencies of existing approximate approaches, and (ii) provides theoretical guaranteed results with high efficiency in both time- and space-perspectives. We demonstrate the superiority of our proposal by extensive experimental evaluation on real datasets. Highlights: We reveal the trade-off strategies among the approximate approaches for IMP . An effective and efficient approximate algorithm called RCELF is proposed. The performance of RCELF is extensively evaluated on 5 real-world datasets.