Special invited paper: The SCORE normalization, especially for heterogeneous network and text data
Special invited paper: The SCORE normalization, especially for heterogeneous network and text data
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DOI：
10.1002/sta4.545
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发表时间：
2023-01-01
期刊：
影响因子：
1.7
通讯作者：
Jin, Jiashun
中科院分区：
文献类型：
--
作者：
Ke, Zheng Tracy;Jin, Jiashun
SCORE作为一种频谱方法被引入到网络社区检测中。由于许多网络具有严重的非均质性，普通谱聚类（OSC）方法在社区检测中可能表现不理想。SCORE通过在谱域引入一种新的归一化思想，缓解了程度异质性的影响，使OSC更加有效。SCORE易于使用，计算速度快。它很容易适应新的方向，并在实践中看到越来越多的兴趣。在本文中，我们回顾了SCORE的基础知识，SCORE在网络混合隶属度估计和主题建模中的应用，以及SCORE在实际数据中的应用，包括两个统计学家出版物的数据集。我们还回顾了SCORE背后的理论“意识形态”。我们证明了在谱域中，SCORE将简单锥转换为单纯形，并在单纯形和网络成员之间提供了简单而直接的联系。SCORE在社区检测中实现了指数速率和急剧的相变，在混合隶属估计和主题建模中实现了最优速率。
SCORE was introduced as a spectral approach to network community detection. Since many networks have severe degree heterogeneity, the ordinary spectral clustering (OSC) approach to community detection may perform unsatisfactorily. SCORE alleviates the effect of degree heterogeneity by introducing a new normalization idea in the spectral domain and makes OSC more effective. SCORE is easy to use and computationally fast. It adapts easily to new directions and sees an increasing interest in practice. In this paper, we review the basics of SCORE, the adaption of SCORE to network mixed membership estimation and topic modeling, and the application of SCORE in real data, including two datasets on the publications of statisticians. We also review the theoretical "ideology" underlying SCORE. We show that in the spectral domain, SCORE converts a simplicial cone to a simplex and provides a simple and direct link between the simplex and network memberships. SCORE attains an exponential rate and a sharp phase transition in community detection, and achieves optimal rates in mixed membership estimation and topic modeling.