Profit Maximization in Closed Social Networks

TL;DR

This paper addresses profit maximization in closed social networks for viral marketing, proposing new algorithms that outperform baselines in real-world datasets by considering limited diffusion links and seed selection within a budget.

Contribution

It introduces the PMCSN problem, generalizes influence maximization, and proposes sampling-based and heuristic solutions with analysis and experimental validation.

Findings

Proposed algorithms achieve higher profit than baseline methods.

Analysis of sample complexity, runtime, and space requirements.

Experimental results on real-world datasets validate effectiveness.

Abstract

Diffusion of information, innovation, and ideas is an important phenomenon in social networks. Information propagates through the network and reaches from one person to the next. In many settings, it is meaningful to restrict diffusion so that each node can spread information to only a limited number of its neighbors rather than to all of them. Such social networks are called closed social networks. In recent years, social media platforms have emerged as an effective medium for commercial entities, where the objective is to maximize profit. In this paper, we study the Profit Maximization in Closed Social Networks (PMCSN) problem in the context of viral marketing. The input to the problem is a closed social network and two positive integers $\ell$ and $B$. The problem asks to select seed nodes within a given budget $B$; during the diffusion process, each node is restricted to choose at most $\ell$ outgoing links for information diffusion; and the objective is to maximize the profit earned by the seed set. The PMCSN problem generalizes the Influence Maximization problem, which is NP-hard. We propose two solution approaches for PMCSN: a sampling-based approximate solution and a marginal-gain-based heuristic solution. We analyze the sample complexity, running time, and space requirements of the proposed approaches. We conduct experiments on real-world, publicly available social network datasets. The results show that the seed sets and diffusion links chosen by our methods yield higher profit than baseline methods. The implementation and data are available at \texttt{https://github.com/PoonamSharma-PY/ClosedNetwork}.