To find where a rumor started, look for the least connected network.
April 17, 2026
Original Paper
Nonlinear dynamics of information overload: Impact on source localization in complex networks
arXiv · 2604.14778
The Takeaway
Standard network theory says more connectivity is better for tracing information, but once a system hits 'information overload,' that logic flips. In dense networks, the sheer volume of data creates noise that masks the original signal's path. This study found that less connected networks actually make it easier to pinpoint a signal's source because there are fewer competing paths to muddy the waters. It’s a complete reversal of how we think about efficiency and transparency in communication. This suggests that in our hyper-connected world, more links might actually be helping the originators of misinformation stay hidden in the crowd.
From the abstract
Source localization in complex networks is a rapidly advancing field with numerous real-world applications, including determining the source of misinformation. In this work, we model information spread across several real-world and synthetic complex networks using our Generalized Fractional Susceptible-Infected-Recovered (GFSIR) model, which incorporates the information overload (IOL) phenomenon. Then, we use Pearson's correlation algorithm to identify information sources in these networks and i