A subjective evidence model for influence maximization in social networks

Mohammadreza Samadi, Alexander Nikolaev, Rakesh Nagi

Research output: Contribution to journalArticlepeer-review


This paper introduces the notion of subjective evidence, which fuels a new parallel cascade influence propagation model. The model sheds light on the phenomena of belief reinforcement and viral spread of innovations, rumors, opinions, etc., in social networks. Network actors are assumed to be testing a Bayesian hypothesis, e.g., for making judgment about the superiority of some product(s) or service(s) over others, or (dis)utility of a given program/policy. The model-based influence maximization solutions inform the strategies for market niche selection and protection, and identification of susceptible groups in political campaigning. The NP-Hard problem of influential seed selection is first solved as a mixed-integer program. Second, an efficient Lagrangian Relaxation heuristic with guaranteed bounds is presented. In small, medium and large-scale computational investigations, we analyze: (1) how the success of an influence cascade triggered in a (sub)community, long exposed to an opposite belief, depends on the structural properties of the underlying social network, (2) to what extent growing (increasing the density of) a consumer network within a market niche helps a company protect the niche, (3) given a competitor's strength, when a company should counter the competitor on "their turf", and when and how it should look for limited-time opportunities to maximally profit before eventually surrendering the market.

Original languageEnglish (US)
Pages (from-to)263-278
Number of pages16
JournalOmega (United Kingdom)
StatePublished - Mar 1 2016


  • Bayesian inference
  • Evidence
  • Influence maximization
  • Seed selection
  • Social networks

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Information Systems and Management


Dive into the research topics of 'A subjective evidence model for influence maximization in social networks'. Together they form a unique fingerprint.

Cite this