Network Diffusions via Neural Mean-Field Dynamics
This addresses network diffusion modeling for applications like influence maximization, representing a novel method for a known bottleneck rather than incremental improvement.
The authors tackled the problem of network diffusion inference and estimation by proposing a neural mean-field dynamics framework derived from Mori-Zwanzig formalism, which significantly outperformed existing approaches in accuracy and efficiency on both synthetic and real-world data.
We propose 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 RNN. Directly using cascade data, our framework can jointly learn the structure of the diffusion network and the evolution of infection probabilities, which are cornerstone 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 outperform existing approaches in accuracy and efficiency on both synthetic and real-world data.