A Jump Stochastic Differential Equation Approach for Influence Prediction on Information Propagation Networks

TL;DR

This paper introduces a jump stochastic differential equation framework for modeling and predicting influence in information propagation networks, enabling efficient estimation of activation probabilities and influence levels.

Contribution

It presents a novel jump SDE formulation for continuous-time information propagation, along with an efficient numerical algorithm and theoretical sample complexity bounds.

Findings

More accurate influence prediction on real-world networks

Efficient algorithm with variance reduction improves performance

Applicable to complex and heterogeneous network settings

Abstract

We propose a novel problem formulation of continuous-time information propagation on heterogenous networks based on jump stochastic differential equations (SDE). The structure of the network and activation rates between nodes are naturally taken into account in the SDE system. This new formulation allows for efficient and stable algorithm for many challenging information propagation problems, including estimations of individual activation probability and influence level, by solving the SDE numerically. To this end, we develop an efficient numerical algorithm incorporating variance reduction; furthermore, we provide theoretical bounds for its sample complexity. Moreover, we show that the proposed jump SDE approach can be applied to a much larger class of critical information propagation problems with more complicated settings. Numerical experiments on a variety of synthetic and real-world propagation networks show that the proposed method is more accurate and efficient compared with the state-of-the-art methods.