Encyclopedia of Algorithms

2016 Edition
| Editors: Ming-Yang Kao

Influence Maximization

  • Zaixin LuEmail author
  • Weili Wu
Reference work entry
DOI: https://doi.org/10.1007/978-1-4939-2864-4_710

Years and Authors of Summarized Original Work

  • 2011; Lu, Zhang, Wu, Fu, Du

  • 2012; Lu, Zhang, Wu, Kim, Fu

Problem Definition

One of the fundamental problems in social network is influence maximization. Informally, if we can convince a small number of individuals in a social network to adopt a new product or innovation, and the target is to trigger a maximum further adoptions, then which set of individuals should we convince? Consider a social network as a graph G(V, E) consisting of individuals (node set V ) and relationships (edge set E); essentially influence maximization comes down to the problem of finding important nodes or structures in graphs.

Influence Diffusion

In order to address the influence maximization problem, first it is needed to understand the influence diffusion process in social networks. In other words, how does the influence propagate over time through a social network? Assume time is partitioned into discrete time slots, and then influence diffusion can be modeled...


Approximation algorithm Influence maximization NP-hard Social network 
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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceMarywood UniversityScrantonUSA
  2. 2.College of Computer Science and TechnologyTaiyuan University of TechnologyTaiyuanChina
  3. 3.Department of Computer ScienceCalifornia State UniversityLos AngelesUSA
  4. 4.Department of Computer ScienceThe University of Texas at DallasRichardsonUSA