Towards Profit Maximization for Online Social Network Providers

TL;DR

This paper introduces a novel profit maximization framework for online social network providers that balances influence spread benefits against propagation costs, using a new non-submodular optimization approach validated on real datasets.

Contribution

It proposes a new profit metric combining influence benefits and costs, and develops a two-phase seed selection framework with theoretical bounds for influence maximization.

Findings

Achieves high approximation guarantees in experiments.

Significantly outperforms existing influence maximization algorithms.

Effectively balances influence spread and propagation costs.

Abstract

Online Social Networks (OSNs) attract billions of users to share information and communicate where viral marketing has emerged as a new way to promote the sales of products. An OSN provider is often hired by an advertiser to conduct viral marketing campaigns. The OSN provider generates revenue from the commission paid by the advertiser which is determined by the spread of its product information. Meanwhile, to propagate influence, the activities performed by users such as viewing video ads normally induce diffusion cost to the OSN provider. In this paper, we aim to find a seed set to optimize a new profit metric that combines the benefit of influence spread with the cost of influence propagation for the OSN provider. Under many diffusion models, our profit metric is the difference between two submodular functions which is challenging to optimize as it is neither submodular nor monotone. We design a general two-phase framework to select seeds for profit maximization and develop several bounds to measure the quality of the seed set constructed. Experimental results with real OSN datasets show that our approach can achieve high approximation guarantees and significantly outperform the baseline algorithms, including state-of-the-art influence maximization algorithms.