基于利用邻居属性的随机游走的图特征估计
Estimation of Graph Features Based on Random Walks Using Neighbors' Properties
AI总结：
针对社交网络采样中API调用成本高的问题，提出一种将邻居节点获取视为成本因素的随机游走图特征估计算法，实验验证其准确性优于现有方法。
摘要：
在评估大型未知社交网络的特征时，使用随机游走进行采样已被证明具有优势。近年来，研究人员提出了多种基于随机游走的算法。在社交网络采样的实际应用中，通常需要依赖应用程序编程接口（API）来获取相邻节点。但由于查询频率限制及相关的API成本，在特征估计过程中最好尽量减少API调用。本研究将获取邻居节点视为一项成本因素，提出了一种特征估计算法，其准确性优于现有算法。通过在已知图上模拟采样的实验，我们证明了所提算法相比现有备选方案具有更优的准确性。
英文摘要：
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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