arXiv:2611.13919 · v1

EAGLE: Early Identification of Information Propagation Sources through Generalized Bayesian Inference in Large-scale Social Networks

首次提交 2026-11-10 19:07

版本更新 2026-11-10 19:07

雷达观测 2026-11-11 11:04

摘要

In the era of infodemics, rapidly pinpointing information propagation sources is essential for mitigating negative impacts and enhancing societal resilience. This study presents EAGLE (Early identificAtion of propagation sources through GeneraLized Bayesian infErence), a novel and effective framework for early source identification in large-scale social networks, which integrates evolutionary computation to strategically deploy sparse observers with generalized Bayesian inference to aggregate observational data of varying credibility. Unlike existing approaches, our method relies on limited input from human observers and explicitly incorporates the uncertainty and noise of the data. Comprehensive evaluations across diverse networks and propagation scenarios demonstrate that our approach consistently surpasses state-of-the-art models, without relying on assumptions about propagation characteristics or user attributes. Utilizing real-world datasets with empirical propagation paths from Twitter, our method is among the first capable of identifying potential sources in large-scale networks using limited observers, demonstrating its broad applicability. These findings highlight the effectiveness of generalized Bayesian inference in uncovering propagation dynamics and equip organizations and policymakers with critical tools to proactively address threats to digital trust and security.

作者备注：24 pages, 8 figures