Social networks have become widely used platforms for their users to share information. Learning the information diffusion process is essential for successful applications of viral marketing and cyber security in social media networks. This paper proposes two learning models that are aimed at learning person-to-person influence in information diffusion from historical cascades based on the threshold propagation model. The first model is based on the linear threshold propagation model. In addition, by considering multi-step information propagation in one time period, this paper proposes a learning model for multi-step diffusion influence between pairs of users based on the idea of random walk. Mixed integer programs (MIP) have been used to learn these models by minimizing the prediction errors, where decision variables are estimations of the diffusion influence between pairs of users. For large-scale networks, this paper develops approximate methods for those learning models by using artificial neural networks to learn the pairwise influence. Extensive computational experiments using both synthetic data and real data have been conducted to demonstrate the effectiveness of the proposed models and methods.

中文翻译：

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社交媒体网络中基于模型的信息传播学习

社交网络已成为其用户共享信息的广泛使用的平台。学习信息传播过程对于在社交媒体网络中成功应用病毒式营销和网络安全至关重要。本文提出了两种学习模型，旨在基于阈值传播模型来学习历史级联中信息传播中的人与人之间的影响。第一个模型基于线性阈值传播模型。此外，通过考虑一个时间段内的多步信息传播，基于随机游走的思想，提出了一种针对用户对之间多步扩散影响的学习模型。混合整数程序（MIP）已用于通过最小化预测误差来学习这些模型，其中决策变量是对用户对之间的扩散影响的估计。对于大型网络，本文通过使用人工神经网络学习成对影响力，为这些学习模型开发了近似方法。已经进行了使用合成数据和真实数据的大量计算实验，以证明所提出的模型和方法的有效性。