Activity Maximization by Effective Information Diffusion in Social Networks

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

37 Scopus Citations
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Author(s)

  • Zhefeng Wang
  • Yu Yang
  • Jian Pei
  • Lingyang Chu
  • Enhong Chen

Detail(s)

Original languageEnglish
Article number8010858
Pages (from-to)2374-2387
Journal / PublicationIEEE Transactions on Knowledge and Data Engineering
Volume29
Issue number11
Online published15 Aug 2017
Publication statusPublished - Nov 2017
Externally publishedYes

Abstract

In a social network, even about the same information the excitement between different users are different. If we want to spread a piece of new information and maximize the expected total amount of excitement, which seed users should we choose? This problem indeed is substantially different from the renowned influence maximization problem and cannot be tackled using the existing approaches. In this paper, motivated by the demand in a few interesting applications, we model the novel problem of activity maximization, and tackle the problem systematically. We first analyze the complexity and the approximability of the problem. We develop an upper bound and a lower bound that are submodular so that the Sandwich framework can be applied. We then devise a polling-based randomized algorithm that guarantees a data dependent approximation factor. Our experiments on four real data sets clearly verify the effectiveness and scalability of our method, as well as the advantage of our method against the other heuristic methods.

Research Area(s)

  • activity maximization, information diffusion, social influence, Social network

Citation Format(s)

Activity Maximization by Effective Information Diffusion in Social Networks. / Wang, Zhefeng; Yang, Yu; Pei, Jian et al.

In: IEEE Transactions on Knowledge and Data Engineering, Vol. 29, No. 11, 8010858, 11.2017, p. 2374-2387.

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