A two-stage stochastic programming approach for influence maximization in social networks

Hao-Hsiang Wu, Simge Küçükyavuz*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

We consider stochastic influence maximization problems arising in social networks. In contrast to existing studies that involve greedy approximation algorithms with a 63% performance guarantee, our work focuses on solving the problem optimally. To this end, we introduce a new class of problems that we refer to as two-stage stochastic submodular optimization models. We propose a delayed constraint generation algorithm to find the optimal solution to this class of problems with a finite number of samples. The influence maximization problems of interest are special cases of this general problem class. We show that the submodularity of the influence function can be exploited to develop strong optimality cuts that are more effective than the standard optimality cuts available in the literature. Finally, we report our computational experiments with large-scale real-world datasets for two fundamental influence maximization problems, independent cascade and linear threshold, and show that our proposed algorithm outperforms the basic greedy algorithm of Kempe et al. (Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, KDD’03, New York, NY, USA, ACM, pp 137–146, 2003).

Original languageEnglish
Pages (from-to)563-595
Number of pages33
JournalComputational Optimization and Applications
Volume69
Issue number3
DOIs
StatePublished - 1 Apr 2018

Keywords

  • Independent cascade
  • Influence maximization
  • Linear threshold
  • Social networks
  • Stochastic programming
  • Submodularity

Fingerprint

Dive into the research topics of 'A two-stage stochastic programming approach for influence maximization in social networks'. Together they form a unique fingerprint.

Cite this