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 at sampling rates not exceeding 10%.

翻译：图采样在大型网络数据挖掘中扮演着重要角色。具体而言，大型网络通常对应较低的采样率。在此情境下，传统基于遍历的采样方法往往对网络核心节点存在过度偏好。针对该问题，本文提出一种适用于低采样率下未知网络的采样方法SLSR，该方法首先采用随机节点采样来估计度阈值（用于区分核心与边缘区域）以及未知网络的平均度，随后在核心与边缘区域上执行双层采样策略。SLSR方法简洁高效，实验评估证实，在不超过10%的采样率下，该方法能够准确保留未知大型网络的诸多关键结构特征。