2018/05/05 by Rahul Goel, Anurag Singh, Fakhteh Ghanbarnejad · 18 citations
Computer Science · Decision Sciences · Mathematics · Physics and Astronomy · #Business #Competition (biology) #Complex Network Analysis Techniques #Computer science #Economics #Function (biology) #Game Theory and Applications #Game theory #Marketing #Mathematics #Microeconomics #Nash equilibrium #Network effect #Node (physics) #Opinion Dynamics and Social Influence #Product (mathematics) #Revenue #Social media #Social network (sociolinguistics) #cs.GT #cs.IT #cs.SI #math.IT
paper · pdf · doi:10.1016/j.physa.2018.11.035
published in Physica A Statistical Mechanics and its Applications 518, 50-70 (Elsevier BV)
arxiv created 2018/05/05 · openalex created_date 2018/05/17 · openalex publication_date 2018/11/27 · arxiv updated 2019/01/01 · openalex updated_date 2026/08/05
In a competitive marketing, there are a large number of players which produce the same product. Each firm aims to diffuse its product information widely so that it's product will become popular among potential buyers. The more popular is a product of a firm, the higher is the revenue for the firm. A model is developed in which two players compete to spread information in the large network. Players choose their initial seed nodes simultaneously and the information is diffused according to Independent Cascade model (ICM). The main aim of the player is to choose the seed nodes such that they will spread its information to as many nodes as possible in a social network. The rate of spreading of information also plays a very important role in information diffusion process. Any node in a social network will get influenced by none or one or more than one information. We also analyzed how much fraction of nodes in different compartment changes by changing the rate of spreading of information. Finally, a game theory model is developed to obtain the Nash equilibrium based on best response function of the players. This model is based on Hotelling's model of electoral competition.