Scalable Cost-Aware Multi-Way Influence Maximization

TL;DR

This paper introduces a scalable, cost-aware approach to multi-way influence maximization in social networks, aiming to optimize viral marketing strategies by considering influence costs and large-scale network structures.

Contribution

It proposes a novel scalable algorithm for multi-way influence maximization that incorporates influence costs, improving efficiency and applicability to large social networks.

Findings

The proposed method effectively maximizes influence spread within budget constraints.

Experimental results demonstrate scalability to large social networks.

The approach outperforms existing influence maximization algorithms in efficiency.

Abstract

Viral marketing is different from other marketing strategies since it leverages the influence power in intimate relationship, e.g., close friends, family members, couples. Due to the development and popularity of social networking services, such as Facebook, Twitter, and Pinterest, the new notion of "social media marketing" has appeared in recent years and presents new opportunities for enabling large-scale and prevalent viral marketing online. To boost the growth of their sales, business is embracing social media in a big way. According to USA Today, the sales of software to run corporate social networks will grow 61\% a year and be a $6.4$ billion business by 2016.