DeepPM: 一种基于深度学习的社会网络中利润最大化方法
DeepPM: A Deep Learning-based Profit Maximization Approach in Social Networks
AI总结：
DeepPM提出一种基于深度学习的社会网络利润最大化方法，通过学习多样化的信息扩散模式，提升种子集选择的利润效果。
摘要：
利润最大化问题要求从给定的社会网络中选择有限数量的有影响力用户，使得这些用户的初始激活在扩散过程结束时获得最大利润。该问题对社交网络中的病毒营销有直接冲击。过去十年中，已经开发出几种传统方法（即非学习方法，包括近似解、启发式解等），其中许多方法产生了有希望的结果。所有这些方法都需要信息扩散模型作为输入。然而，将任何特定的扩散模型视为现实世界中的扩散场景可能并不现实，因为现实世界的扩散场景会更加复杂，并不遵循任何特定扩散模型的规则。在本文中，我们提出了一种基于深度学习的框架来解决利润最大化问题。我们的模型对种子集进行了潜在表示，并能够学习多样化的信息扩散模式。我们还设计了一个高尚的目标函数，可以使用所提出的基于学习的方法有效优化。所提出的模型已使用真实世界数据集进行评估，并报告了结果。我们比较了所提出方法的有效性与许多现有方法，并观察到所提出基于学习的方法所选择的种子集比现有方法能获得更多的利润。整个实现和模拟代码可在：https://github.com/PoonamSharma-PY/DeepPM 上找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
