Sampling unknown large networks restricted by low sampling rates

TL;DR

This paper introduces SLSR, a novel sampling method for large unknown networks at low sampling rates, which effectively preserves network structures by distinguishing core and periphery nodes.

Contribution

The paper presents a simple, efficient sampling approach that accurately captures key network structures in unknown large networks with low sampling rates.

Findings

SLSR accurately preserves critical network structures.

SLSR operates efficiently with high time performance.

Experimental results confirm low variance and high accuracy.

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 experimental evaluation confirms that the proposed method can accurately preserve many critical structures of unknown large networks with low sampling rates and low variances.