基于随机漫步的图形特征估算(利用邻域属性

Tsuyoshi Hasegawa, Shiori Hironaka, Kazuyuki Shudo
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引用次数: 0

摘要

事实证明,使用随机游走进行抽样在评估大型未知社交网络的特征方面具有优势。近年来,已经推出了几种基于随机游走的算法。在社交网络采样的实际应用中,人们经常依赖应用编程接口(API)来获取相邻节点。然而,由于查询频率和相关 API 费用的限制,在特征估计过程中最好尽量减少 API 调用。在本研究中,考虑到获取相邻节点的成本因素,我们引入了一种在准确性方面优于现有算法的特征估计算法。通过在已知图上模拟采样的实验,我们证明了与现有算法相比,我们提出的算法具有更高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of Graph Features Based on Random Walks Using Neighbors' Properties
Using random walks for sampling has proven advantageous in assessing the characteristics of large and unknown social networks. Several algorithms based on random walks have been introduced in recent years. In the practical application of social network sampling, there is a recurrent reliance on an application programming interface (API) for obtaining adjacent nodes. However, owing to constraints related to query frequency and associated API expenses, it is preferable to minimize API calls during the feature estimation process. In this study, considering the acquisition of neighboring nodes as a cost factor, we introduce a feature estimation algorithm that outperforms existing algorithms in terms of accuracy. Through experiments that simulate sampling on known graphs, we demonstrate the superior accuracy of our proposed algorithm when compared to existing alternatives.
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