基于蒙特卡洛算法的网络社群检测

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY
Wei Yu
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引用次数: 0

摘要

社群检测是网络数据分析中的一个重要问题。本文通过最小化基于邻接矩阵与其期望值之差的目标函数来实现社群检测,并解释了目标函数的合理性。为了解决优化问题,我们提出了一种新算法,该算法参考了马尔可夫链蒙特卡罗和随机模拟领域中低差异序列的思想。我们引入了一个新指标,通过测量真实社区与估计社区的相似度来比较各种方法的性能。我们分析了合成网络和真实网络,以研究新方法的有效性。结果表明,在所有模拟场景中,建议方法的性能都很稳定。在大多数情况下,它都优于现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Community detection for networks based on Monte Carlo type algorithms

Community detection for networks based on Monte Carlo type algorithms

The community detection is a significant problem in network data analysis. In this paper, we implement community detection by minimizing an objective function based on the difference between the adjacency matrix and its expected value, and explain the rationality of the objective function. To solve the optimization problem, we propose a new algorithm which is referred to the thoughts of Markov Chain Monte Carlo and low discrepancy sequence in the random simulation fields. We introduce a new indicator to compare the performance of the methods by measuring the similarity of the true community and the estimated community. Synthetic networks and real networks are analyzed to investigate the effectiveness of the new method. Results show that the performance of the proposed method is stable in all simulated scenarios. And in most cases, it outperforms existing methods.

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来源期刊
Journal of the Korean Statistical Society
Journal of the Korean Statistical Society 数学-统计学与概率论
CiteScore
1.30
自引率
0.00%
发文量
37
审稿时长
3 months
期刊介绍: The Journal of the Korean Statistical Society publishes research articles that make original contributions to the theory and methodology of statistics and probability. It also welcomes papers on innovative applications of statistical methodology, as well as papers that give an overview of current topic of statistical research with judgements about promising directions for future work. The journal welcomes contributions from all countries.
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