The classifier judged this item not relevant to AI security, so it is left out of lists, feeds, the API and dataset releases. If that is wrong, report a correction on this page.
InfoResearchPeer-reviewed
Flexible Modeling of Information Diffusion on Networks with Statistical Guarantees
- Published
- Record updated
Summary
This paper develops a likelihood-based method to estimate edge weights from observed information diffusion paths under a proposed General Linear Threshold (GLT) model, a discrete-time class that includes the linear threshold (LT) and independent cascade (IC) models. The authors derive a finite-sample error bound and show the estimator is asymptotically normal under mild conditions. Experiments on synthetic and real-world networks report improved estimation of spread, node activation prediction, and Influence Maximization solution quality.