Recurrent neural variational model for follower-based influence maximization

Huang, H., Meng, Z. and Liang, S. (2020) Recurrent neural variational model for follower-based influence maximization. Information Sciences, 528, pp. 280-293. (doi: 10.1016/j.ins.2020.04.023)

Abstract

Influence Maximization, aiming at selecting a small set of seed users in a social network to maximize the spread of influence, has attracted considerable attention recently. Most of the existing influence maximization algorithms focus on the diffusion model of one single-entity, which assumes that only one entity is propagated by users in social network. However, the diffusion situations in real world social networks often involve multiple entities, competitive or complementary, spreading through the whole network, and are more complex than the situations of single independent entity. In this paper, we propose a novel optimization problem, namely, the follower-based influence maximization, which aims to promote a new product into the market by maximizing the influence of a social network where other competitive and complementary products have already been propagating. We tackle this problem by proposing a Recurrent Neural Variational model (RNV) and a follower-based greedy algorithm (RNVGA). The RNV model dynamically tracks entity correlations and cascade correlations through a deep generative model and recurrent neural variational inference, while the RNVGA algorithm applies the greedy approach for submodular maximization and efficiently computes the seed node set for the target product. Extensive experiments have been conducted to evaluate effectiveness and efficiency of our method, and the results show the superiority of our method compared with the state-of-the-art methods.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Meng, Dr Zaiqiao

Creator Roles

Meng, Z.: Validation, Formal analysis, Writing – review and editing, Supervision

• Authors: Huang, H., Meng, Z., and Liang, S.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Information Sciences
• Publisher: Elsevier
• ISSN: 0020-0255
• ISSN (Online): 1872-6291
• Published Online: 15 April 2020