# Sampling unknown large networks restricted by low sampling rates

arXiv: https://arxiv.org/abs/2308.14279 · HTML: https://mlsanity.com/paper/2308.14279/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2023.

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

- [Hierarchical graph sampling based minibatch learning with chain preservation and variance reduction](https://mlsanity.com/paper/2503.00860/) (2025)
- [Partially Observed Sparse Graphs: The Unknown Sampling Rate is a Tail Index](https://mlsanity.com/paper/2609.26199/) (2026)

## Abstract (arXiv)

Graph sampling plays an important role in data mining for large networks. Specifically, larger networks often correspond to lower sampling rates. Under the situation, traditional traversal-based samplings for large networks usually have an excessive preference for densely-connected network core nodes. Aim at this issue, this paper proposes a sampling method for unknown networks at low sampling rates, called SLSR, which first adopts a random node sampling to evaluate a degree threshold, utilized to distinguish the core from periphery, and the average degree in unknown networks, and then runs a double-layer sampling strategy on the core and periphery. SLSR is simple that results in a high time efficiency, but experimental evaluation confirms that the proposed method can accurately preserve many critical structures of unknown large networks with low sampling rates and low variances.