A Reverse Reachable Set Based Approach for Motif Oriented Profit maximization in Social Networks

TL;DR

This paper introduces a novel approach using reverse reachable sets to maximize profit in social networks by selecting influential nodes based on motif-oriented benefits, addressing a complex NP-hard problem.

Contribution

It proposes a new motif-oriented profit maximization framework and a reverse reachable set-based algorithm to effectively select seed nodes for profit maximization.

Findings

The proposed method outperforms existing approaches in profit maximization.

The framework is scalable and efficient on real-world social network datasets.

Seed sets chosen by the method lead to higher profit gains.

Abstract

Profit Maximization is one of the key objectives for social media marketing, where the task is to choose a limited number of highly influential nodes such that their initial activation leads to maximum profit. In this paper, we introduce a variant of the Profit Maximization Problem where we consider that instead of nodes, benefits are assigned to some of the motifs of the graph, and these benefit values can be earned once a given threshold count of nodes from the motifs is influenced. The goal here is to choose a limited number of nodes for initial activation called seed nodes such that the motif-oriented profit gets maximized. Formally, we call our problem the Motif Oriented Profit Maximization Problem. We show that the problem is NP-hard to solve optimally. We propose a Reverse Reachable Set-based framework to solve our problem. The proposed methodology broadly divides into three steps: KPT Estimation and RR Set generation, Seed Set Selection, and Motif Oriented Profit Estimation. The proposed methodology has been analyzed to understand its time and space requirements. It has been implemented with real-world social network datasets, and the results are reported. We observe that the seed set selected by the proposed solution approaches leads to more profit compared to the seed sets selected by the existing methods. The whole implementation and data are available at: https://github.com/PoonamSharma-PY/MotifProfit.