# Archaeology of random recursive dags and Cooper-Frieze random networks

arXiv: https://arxiv.org/abs/2207.14601 · HTML: https://mlsanity.com/paper/2207.14601/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 29 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Finding Adam in random growing trees](https://mlsanity.com/paper/1411.3317/) (2014)
- [Root and community inference on the latent growth process of a network](https://mlsanity.com/paper/2107.00153/) (2021)
- [From trees to seeds: on the inference of the seed from large trees in the uniform attachment model](https://mlsanity.com/paper/1409.7685/) (2014)
- [Confidence Sets for the Source of a Diffusion in Regular Trees](https://mlsanity.com/paper/1510.05461/) (2015)
- [Persistence of centrality in random growing trees](https://mlsanity.com/paper/1511.01975/) (2015)
- [Analysis of centrality in sublinear preferential attachment trees via the CMJ branching process](https://mlsanity.com/paper/1601.06448/) (2016)
- [On the discovery of the seed in uniform attachment trees](https://mlsanity.com/paper/1810.00969/) (2018)
- [On the influence of the seed graph in the preferential attachment model](https://mlsanity.com/paper/1401.4849/) (2014)

## Built on by (most cited first, 4 of 4)

- [Broadcasting in random recursive dags](https://mlsanity.com/paper/2306.01727/) (2023)
- [Estimating the history of a random recursive tree](https://mlsanity.com/paper/2403.09755/) (2024)
- [Leaf Stripping on Uniform Attachment Trees](https://mlsanity.com/paper/2410.06481/) (2024)
- [History estimation in random recursive trees: Pointwise approach via iterated Jordan centralities](https://mlsanity.com/paper/2606.24465/) (2026)

## Abstract (arXiv)

We study the problem of finding the root vertex in large growing networks. We prove that it is possible to construct confidence sets of size independent of the number of vertices in the network that contain the root vertex with high probability in various models of random networks. The models include uniform random recursive dags and uniform Cooper-Frieze random graphs.