Influence Estimation and Maximization via Neural Mean-Field Dynamics

TL;DR

This paper introduces a neural mean-field framework for influence estimation and maximization in diffusion networks, combining advanced mathematical formalism with efficient learning algorithms to improve accuracy and robustness.

Contribution

It develops a novel neural mean-field approach using Mori-Zwanzig formalism for simultaneous network structure learning and influence maximization, with fast gradient computation.

Findings

Outperforms existing methods in accuracy and efficiency

Robust across various diffusion network models

Effective on both synthetic and real-world data

Abstract

We propose a novel learning framework using neural mean-field (NMF) dynamics for inference and estimation problems on heterogeneous diffusion networks. Our new framework leverages the Mori-Zwanzig formalism to obtain an exact evolution equation of the individual node infection probabilities, which renders a delay differential equation with memory integral approximated by learnable time convolution operators. Directly using information diffusion cascade data, our framework can simultaneously learn the structure of the diffusion network and the evolution of node infection probabilities. Connections between parameter learning and optimal control are also established, leading to a rigorous and implementable algorithm for training NMF. Moreover, we show that the projected gradient descent method can be employed to solve the challenging influence maximization problem, where the gradient is computed extremely fast by integrating NMF forward in time just once in each iteration. Extensive empirical studies show 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.