Scientific Reports (Jun 2024)

Sampling unknown large networks restricted by low sampling rates

  • Bo Jiao

DOI
https://doi.org/10.1038/s41598-024-64018-3
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 19

Abstract

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Abstract 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 experiments verify that the proposed method can accurately preserve many critical structures of unknown large scale-free networks with low sampling rates and low variances.

Keywords