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We propose a novel problem formulation of continuous-time informationpropagation on heterogenous networks based on jump stochastic differentialequations (SDE). The structure of the network and activation rates betweennodes are naturally taken into account in the SDE system. This new formulationallows for efficient and stable algorithm for many challenging informationpropagation problems, including estimations of individual activationprobability 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 largerclass of critical information propagation problems with more complicatedsettings. Numerical experiments on a variety of synthetic and real-worldpropagation networks show that the proposed method is more accurate andefficient compared with the state-of-the-art methods.

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