考虑私有节点的社交网络随机游走性质估计
Estimating Properties of Social Networks via Random Walk considering Private Nodes
摘要：
准确分析社交网络的图属性是一项具有挑战性的任务，因为对图数据的访问限制。为了解决这一挑战，已经研究了几种通过随机游走从少量样本中获得无偏估计的算法。然而，现有的算法没有考虑真实社交网络中隐藏邻居的私有节点，导致了一些实际问题。在这里，我们设计了基于随机行走的算法来准确地估计属性，而不会出现私有节点引起的任何问题。首先，我们设计了一个基于随机行走的抽样算法，包括邻居选择，以获得具有马尔可夫性质的样本和计算权重，为每个样本，以纠正抽样偏差。此外，对于两个图属性估计器，我们提出了加权方法，以减少不仅采样偏差，但由于私人节点的估计误差。所提出的算法提高了现有算法的估计精度高达92.6%的真实世界的数据集。
英文摘要：
Accurately analyzing graph properties of social networks is a challenging task because of access limitations to the graph data. To address this challenge, several algorithms to obtain unbiased estimates of properties from few samples via a random walk have been studied. However, existing algorithms do not consider private nodes who hide their neighbors in real social networks, leading to some practical problems. Here we design random walk-based algorithms to accurately estimate properties without any problems caused by private nodes. First, we design a random walk-based sampling algorithm that comprises the neighbor selection to obtain samples having the Markov property and the calculation of weights for each sample to correct the sampling bias. Further, for two graph property estimators, we propose the weighting methods to reduce not only the sampling bias but also estimation errors due to private nodes. The proposed algorithms improve the estimation accuracy of the existing algorithms by up to 92.6% on real-world datasets.
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