# Computational Models for Commercial Advertisements in Social Networks

## TL;DR

This paper introduces an improved k-shell algorithm for identifying influential spreaders in social networks, demonstrating its effectiveness through simulations that show up to 36% larger reach compared to existing methods.

## Contribution

We propose an extended k-shell algorithm that enhances influencer detection by leveraging topological features, outperforming traditional metrics in viral spread simulations.

## Key findings

- Extended k-shell reaches 36% larger crowds
- Simulation results validate improved influencer detection
- Method outperforms degree and eigenvector centrality

## Abstract

Identifying noteworthy spreaders in a network is essential for understanding the spreading process and controlling the reach of the spread in the network. The nodes that are holding more intrinsic power to extend the reach of the spread are important due to demand for various applications such as viral marketing, controlling rumor spreading or get a better understanding of spreading of the diseases. As an application of the viral marketing, maximization of the reach with a fixed budget is a fundamental requirement in the advertising business. Distributing a fixed number of promotional items for maximizing the viral reach can leverage influencer detection methods. For detecting such "influencer" nodes, there are local metrics such as degree centrality (mostly used as in-degree centrality) or global metrics such as k-shell decomposition or eigenvector centrality. All the methods can rank graphs but they all have limitations and there is still no de-facto method for influencer detection in the domain. In this paper, we propose an extended k-shell algorithm which better utilizes the k-shell decomposition for identifying viral spreader nodes using the topological features of the network. We use Susceptible-Infected-Recovered model for the simulations of the spreading process in real-life networks and the simulations demonstrates that our approach can reach to up to 36% larger crowds within the same network, with the same number of initial spreaders.