A Jump Stochastic Differential Equation Approach for Influence Prediction on Information Propagation Networks
This work provides a new theoretical framework and practical algorithm for influence prediction in information propagation networks, addressing scalability and accuracy bottlenecks.
The authors propose a jump SDE formulation for continuous-time information propagation on heterogeneous networks, enabling efficient estimation of activation probabilities and influence levels. Their numerical algorithm with variance reduction achieves higher accuracy and efficiency than state-of-the-art methods on synthetic and real-world networks.
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.