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.

翻译：图抽样在大规模网络数据挖掘中扮演着重要角色。具体而言，规模越大的网络往往对应越低的采样率。在此情况下，针对大型网络的传统遍历式抽样通常过度偏好网络核心节点。针对该问题，本文提出一种面向低采样率的未知网络抽样方法SLSR，该方法首先通过随机节点抽样评估度阈值（用于区分网络核心与边缘节点）及未知网络的平均度值，随后对核心层与边缘层实施双层抽样策略。SLSR方法结构简洁，具有较高时间效率，但实验评估证实，该方法能够在低采样率与低方差条件下，准确保留未知大型网络的诸多关键结构特征。