Predicting Multi-Scale Information Diffusion via Minimal Substitution Neural Networks

In social media platforms such as Weibo, Twitter, and Facebook, a variety of information is diffused daily. Exploring and exploiting the diffusion patterns in this information play crucial roles in areas such as viral marketing, recommendation systems, and public opinion management. However, the diffusion of this information is not merely sequential propagation among users, as most researchers assume. When we observe the diffusion of information in the entire network from a macroscopic perspective, we discover that these phenomena of information diffusion exhibit a series of interconnected relationships, such as alternation or dependency. In traditional methods of information diffusion prediction (IDP), these aspects are often overlooked. To address this, we introduce a substitution theory of information diffusion, minimal substitution (MS), and we combine it with neural networks to design a network model known as MSNN. First, the incorporation of MS theory enables our model to effectively capture the complex interrelations among pieces of information. Second, we analyze the relationship of the multi-scale IDP task, develop a one-step MS-based microscopic IDP method and a dynamic MS-based macroscopic IDP method, and utilize these two methods for joint training to achieve multi-scale prediction. Finally, we validate the accuracy of the proposed MSNN model through training on two real-world datasets with different growth patterns.