Submodular Inference of Diffusion Networks from Multiple Trees

TL;DR

This paper introduces a scalable approximation algorithm for inferring dynamic diffusion networks from limited cascade data, leveraging submodular maximization to achieve high accuracy and efficiency.

Contribution

It presents a novel, scalable, near-optimal algorithm for inferring evolving diffusion networks from small cascade sets, addressing an open problem.

Findings

Achieves high accuracy in network inference from limited data

Balances inference accuracy with computational efficiency

Validated on synthetic and real-world diffusion data

Abstract

Diffusion and propagation of information, influence and diseases take place over increasingly larger networks. We observe when a node copies information, makes a decision or becomes infected but networks are often hidden or unobserved. Since networks are highly dynamic, changing and growing rapidly, we only observe a relatively small set of cascades before a network changes significantly. Scalable network inference based on a small cascade set is then necessary for understanding the rapidly evolving dynamics that govern diffusion. In this article, we develop a scalable approximation algorithm with provable near-optimal performance based on submodular maximization which achieves a high accuracy in such scenario, solving an open problem first introduced by Gomez-Rodriguez et al (2010). Experiments on synthetic and real diffusion data show that our algorithm in practice achieves an optimal trade-off between accuracy and running time.