Abstract
The Reverse Influence Maximization (RIM) model deals with the viral marketing cost minimization in social networks. On the other hand, the Influence maximization (IM) technique finds the small number of influential users that maximize the viral marketing profit. Here, the profit is defined by the maximum number of nodes that can be influenced by seed users when they are initially activated. On the other hand, the cost is measured by the minimum number of nodes required to activate all the seed nodes. However, most of the existing studies focus on profit maximization without considering the seeding cost. Moreover, the most profitable node may not always be a cost-effective one. Thus, in this research, we introduce an epidemic Cost Minimization model under the Competitive Market in Dynamic social networks (CMCMD, i.e., CM2D). In the CM2D model, we modify the Susceptible-Infected-Recovered (SIR) technique as the Reverse SIR (RSIR) model to employ in the node activation process. The proposed CM2D model also uses the greedy Set Cover approximation technique to optimize the seeding cost. Moreover, the CM2D model tackles the challenging issues of the RIM problem more efficiently than the existing models. Finally, we simulate our model using datasets of two synthesized and four real datasets, and the results show that the CM2D model outperforms the existing models.
Highlights
With the rapid expansion of Twitter, Facebook, Instagram, Flickr-like social networks, people are increasingly using social networks to share information, news, trends, and innovations
1) We introduce the Reverse SIR (RSIR) model, which is a variant of the SIR model to estimate the seeding cost
Proof 1: We prove that the cost minimization problem under the CM2D model stated in Algorithm 2 is a variant of the Minimum Set Cover (MSC) algorithm presented in Algorithm 3 [53]
Summary
With the rapid expansion of Twitter, Facebook, Instagram, Flickr-like social networks, people are increasingly using social networks to share information, news, trends, and innovations. Some Reverse Influence Maximization (RIM) models are available in the literature to optimize the seeding cost [24]–[26]. These studies mention some challenges of the RIM models; the models are incapable of resolving the challenges and providing the optimal seeding cost simultaneously. B. CONTRIBUTIONS in this paper, we propose a Cost Minimization model under the Competitive Market in Dynamic social networks (CMCMD, i.e., CM2D). The proposed model determines the optimized seeding cost as well as resolves the challenging issues simultaneously. 3) We evaluate the performance of the proposed CM2D model using four real datasets of popular social networks along with two synthesized datasets.
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