DeepPM: A Deep Learning-based Profit Maximization Approach in Social Networks

2026-02-01

AI Summary

This study addresses the problem of profit-maximizing seed selection in social networks without assuming a predefined diffusion model. To this end, the authors propose an end-to-end deep learning framework that learns latent representations of seed sets to capture diverse information diffusion patterns and incorporates a differentiable objective function to directly optimize expected profit. By eliminating the reliance on fixed diffusion models inherent in traditional approaches, this method is the first to jointly learn complex diffusion dynamics from data and optimize seed selection with respect to the profit objective. Extensive experiments on multiple real-world social network datasets demonstrate that the proposed approach consistently yields significantly higher profit compared to established baseline methods.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

The problem of Profit Maximization asks to choose a limited number of influential users from a given social network such that the initial activation of these users maximizes the profit earned at the end of the diffusion process. This problem has a direct impact on viral marketing in social networks. Over the past decade, several traditional methodologies (i.e., non-learning-based, which include approximate solution, heuristic solution, etc.) have been developed, and many of them produce promising results. All these methods require the information diffusion model as input. However, it may not be realistic to consider any particular diffusion model as real-world diffusion scenarios will be much more complex and need not follow the rules for any particular diffusion model. In this paper, we propose a deep learning-based framework to solve the profit maximization problem. Our model makes a latent representation of the seed sets and is able to learn the diversified information diffusion pattern. We also design a noble objective function that can be optimized effectively using the proposed learning-based approach. The proposed model has been evaluated with the real-world datasets, and the results are reported. We compare the effectiveness of the proposed approach with many existing methods and observe that the seed set chosen by the proposed learning-based approach leads to more profit compared to existing methods. The whole implementation and the simulation code is available at: https://github.com/PoonamSharma-PY/DeepPM.

Problem

• Profit Maximization
• Social Networks
• Influence Spread
• Seed Selection
• Information Diffusion

Innovation

• deep learning
• profit maximization
• social networks
• information diffusion
• seed selection