Department of Mathematics and Statistics

Georgia State University

Abstract:

We consider a novel learning framework based on neural mean-field dynamics for inference and estimation problems of diffusion on networks. Our new framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities, which renders a delay differential equation with memory integral approximated by learnable time convolution operators, resulting in a highly structured and interpretable recurrent neural network. Directly using cascade data, our framework can jointly learn the structure of the diffusion network and the evolution of infection probabilities, which are cornerstones to important downstream applications such as influence maximization. Connections between parameter learning and optimal control are also established. Empirical study shows that our approach is versatile and robust to variations of the underlying diffusion network models, and significantly outperforms existing approaches in accuracy and efficiency on both synthetic and real-world data. This is a joint work with Shushan He (GSU) and Hongyuan Zha (CUHK-SZ).