# Burstiness and spreading on temporal networks

arXiv: https://arxiv.org/abs/1305.0543 · HTML: https://mlsanity.com/paper/1305.0543/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2013.

## Built on by (most cited first, 2 of 2)

- [Data Distributional Properties Drive Emergent In-Context Learning in Transformers](https://mlsanity.com/paper/2205.05055/) (2022)
- [SINC: Self-Supervised In-Context Learning for Vision-Language Tasks](https://mlsanity.com/paper/2307.07742/) (2023)

## Abstract (arXiv)

We discuss how spreading processes on temporal networks are impacted by the shape of their inter-event time distributions. Through simple mathematical arguments and toy examples, we find that the key factor is the ordering in which events take place, a property that tends to be affected by the bulk of the distributions and not only by their tail, as usually considered in the literature. We show that a detailed modeling of the temporal patterns observed in complex networks can change dramatically the properties of a spreading process, such as the ergodicity of a random walk process or the persistence of an epidemic.