DeepPM: A Deep Learning-based Profit Maximization Approach in Social Networks
Poonam Sharma, Suman Banerjee

TL;DR
DeepPM introduces a deep learning framework for profit maximization in social networks, effectively learning diffusion patterns without relying on specific diffusion models, outperforming traditional methods in real-world datasets.
Contribution
The paper presents a novel deep learning-based approach that models influence diffusion for profit maximization without requiring predefined diffusion models.
Findings
Outperforms existing methods in profit maximization tasks
Learns diversified diffusion patterns effectively
Validated on real-world social network datasets
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…
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Taxonomy
TopicsComplex Network Analysis Techniques · Customer churn and segmentation · Digital Marketing and Social Media
