网络抽样技术的实证比较
Empirical comparison of network sampling techniques
摘要：
在过去的几年里，大规模和快速发展的网络的存储和分析提出了巨大的挑战。因此，已经提出了许多不同的技术来对大型网络进行采样。通常，网络探索技术比随机节点和链路选择更准确地近似原始网络。然而，链接选择与额外的子图归纳步骤优于大多数其他技术。在本文中，我们应用子图归纳也随机行走和森林火灾抽样。我们分析了不同的现实世界中的网络和它们的性质的变化引入采样。我们比较了几种基于原始网络和它们的采样变体之间的匹配的采样技术。结果表明，子图归纳技术低估了原始网络的度和聚类分布，而高估了原始网络的平均度和密度。没有子图归纳步骤的技术表现出完全相反的行为。因此，从随机选择类别的抽样技术的性能相比，网络探索抽样没有显着差异，而有子图归纳步骤和没有它的技术之间存在明显的差异。
英文摘要：
In the past few years, the storage and analysis of large-scale and fast evolving networks present a great challenge. Therefore, a number of different techniques have been proposed for sampling large networks. In general, network exploration techniques approximate the original networks more accurately than random node and link selection. Yet, link selection with additional subgraph induction step outperforms most other techniques. In this paper, we apply subgraph induction also to random walk and forest-fire sampling. We analyze different real-world networks and the changes of their properties introduced by sampling. We compare several sampling techniques based on the match between the original networks and their sampled variants. The results reveal that the techniques with subgraph induction underestimate the degree and clustering distribution, while overestimate average degree and density of the original networks. Techniques without subgraph induction step exhibit exactly the opposite behavior. Hence, the performance of the sampling techniques from random selection category compared to network exploration sampling does not differ significantly, while clear differences exist between the techniques with subgraph induction step and the ones without it.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
