Development of a monopoly pricing model for diffusion maximization in fuzzy weighted social networks with negative externalities of heterogeneous nodes using a case study

Today, informational structure is organized in such a way that sellers can easily employ the various capabilities of social networks, such as the analysis of positive and negative tendencies of neighbours, to maximize diffusion in the network. Therefore, in this paper we employ this approach to introduce a novel mathematical product pricing model for a monopoly product in a non-competitive environment and in the presence of heterogeneous customers. In this model, all customers are divided into various groups based on their preferences for the price, quality and need time for the product demand and also the positive and negative influences of neighbours. So, it seems customers utilize a multi-criteria decision-making model for buying a product. When a customer buys a product and additionally, persuades its neighbours to also buy the product they will receive a referral bonus from the seller. Meanwhile, the intensity of relations between neighbours in the network is incorporated into the model qualitatively. Finally, hardness of the problem justifies application of a genetic algorithm for solving the proposed pricing model and real-world dataset is used to conduct a case study that verifies its applicability.</p

Influence maximization for dynamic allocation in voter dynamics

In this paper, we study the competition between external controllers with fixed campaign budgets in which one of the controllers attempts to maximize the share of a desired opinion in a group of agents who exchange opinions on a social network subject to voting dynamics. In contrast to allocating all the budgets at the beginning of the campaign, we consider a version of a temporal influence maximization problem, where the controller has the flexibility to determine when to start control. We then explore the dependence of optimal starting times to achieve maximum vote shares at a finite time horizon on network heterogeneity. We find that, for short time horizons, maximum influence is achieved by starting relatively later on more heterogeneous networks than in less homogeneous networks, while the opposite holds for long time horizons