Continuous Influence Maximization

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

6 Scopus Citations
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Author(s)

  • Yu Yang
  • Xiangbo Mao
  • Jian Pei
  • Xiaofei He

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number29
Journal / PublicationACM Transactions on Knowledge Discovery from Data
Volume14
Issue number3
Online publishedMar 2020
Publication statusPublished - May 2020

Abstract

Imagine we are introducing a new product through a social network, where we know for each user in the network the function of purchase probability with respect to discount. Then, what discounts should we offer to those social network users so that, under a predefined budget, the adoption of the product is maximized in expectation? Although influence maximization has been extensively explored, this appealing practical problem still cannot be answered by the existing influence maximization methods. In this article, we tackle the problem systematically. We formulate the general continuous influence maximization problem, investigate the essential properties, and develop a general coordinate descent algorithmic framework as well as the engineering techniques for practical implementation. Our investigation does not assume any specific influence model and thus is general and principled. At the same time, using the most popularly adopted triggering model as a concrete example, we demonstrate that more efficient methods are feasible under specific influence models. Our extensive empirical study on four benchmark real-world networks with synthesized purchase probability curves clearly illustrates that continuous influence maximization can improve influence spread significantly with very moderate extra running time comparing to the classical influence maximization methods.

Research Area(s)

  • Budget allocation, Influence maximization, Viral marketing

Bibliographic Note

Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).

Citation Format(s)

Continuous Influence Maximization. / Yang, Yu; Mao, Xiangbo; Pei, Jian et al.

In: ACM Transactions on Knowledge Discovery from Data, Vol. 14, No. 3, 29, 05.2020.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review