计算机科学
采样(信号处理)
Hop(电信)
算法
估计员
重要性抽样
数据挖掘
估计
作者
Yuqing Zhu,Jing Tang,Xueyan Tang,Sibo Wang,Andrew Lim
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2021-07-01
卷期号:: 1-1
标识
DOI:10.1109/tkde.2021.3093934
摘要
With rapidly growing sizes of online social networks, computational challenges arise in analyzing the diffusion process over networks. Sampling methods are commonly used to study the cascade effect and estimate users' influence. In this paper, we propose a brand-new sampling method, called 2-hop+ sampling for quickly and accurately estimating the cascade size generated by a set of seed users under the independent cascade model. Our method generates only samples with at least one 2-hop live path from the source to reduce the number of samples. We further enhance the sampling efficiency of our method by a SkipEdge technique. Moreover, we improve the generalized stopping rule algorithm to obtain an (,)-estimate of the mean of random variables with fewer samples needed. Extensive experiments with real-world datasets show that our techniques can significantly improve the estimation efficiency compared to the state-of-the-art methods.
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