Improvements on SCORE, Especially for Weak Signals
Improvements on SCORE, Especially for Weak Signals
复制标题
SCORE 的改进，特别是对于弱信号
DOI：
10.1007/s13171-020-00240-1
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Luo, Shengming
中科院分区：
文献类型：
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作者：
Jin, Jiashun;Ke, Zheng Tracy;Luo, Shengming
网络可能具有弱信号和严重的程度异质性，并且在一个事件中可能非常稀疏，而在另一个事件中可能非常密集。得分(安。Statist.43，57-89,2015)是一种最新的网络社区检测方法。它适应了严重的程度异构性，并能适应不同程度的稀疏性，但它在弱信号网络中的性能尚不清楚。在本文中，我们证明了在允许弱信号、严重程度异质性和广泛的网络稀疏性的广泛的网络设置中，SCORE实现了完美的聚类，并且在Hamming聚类错误中具有所谓的指数速率。该证明使用了关于网络邻接矩阵的主要特征向量的入口界的最新进展。理论分析表明，Score在弱信号环境下仍能保持较好的性能，但不排除在实际应用中，特别是在弱信号网络中，Score可能会进一步改进，以获得更好的性能。作为论文的第二个贡献，我们提出了SCORE+作为SCORE的改进版本。我们用8个网络数据集对SCORE+进行了研究，发现它的性能优于几种有代表性的方法。特别是，对于信号相对较强的6个数据集，SCORE+的性能与SCORE相似，但对于信号可能较弱的2个数据集(西蒙斯，加州理工大学)，SCORE+的错误率要低得多。Score+建议对Score进行几项更改。我们使用理论和数值研究相结合的方法，仔细解释了这些变化背后的基本原理。
A network may have weak signals and severe degree heterogeneity, and may be very sparse in one occurrence but very dense in another. SCORE (Ann. Statist.43, 57–89, 2015) is a recent approach to network community detection. It accommodates severe degree heterogeneity and is adaptive to different levels of sparsity, but its performance for networks with weak signals is unclear. In this paper, we show that in a broad class of network settings where we allow for weak signals, severe degree heterogeneity, and a wide range of network sparsity, SCORE achieves prefect clustering and has the so-called “exponential rate” in Hamming clustering errors. The proof uses the most recent advancement on entry-wise bounds for the leading eigenvectors of the network adjacency matrix. The theoretical analysis assures us that SCORE continues to work well in the weak signal settings, but it does not rule out the possibility that SCORE may be further improved to have better performance in real applications, especially for networks with weak signals. As a second contribution of the paper, we propose SCORE+ as an improved version of SCORE. We investigate SCORE+ with 8 network data sets and found that it outperforms several representative approaches. In particular, for the 6 data sets with relatively strong signals, SCORE+ has similar performance as that of SCORE, but for the 2 data sets (Simmons, Caltech) with possibly weak signals, SCORE+ has much lower error rates. SCORE+ proposes several changes to SCORE. We carefully explain the rationale underlying each of these changes, using a mixture of theoretical and numerical study.
通过正则化张量幂迭代进行超图网络的社区检测
DOI：
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发表时间：
2019
期刊：
影响因子：
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作者：
Z. Ke;Feng Shi;Dong Xia
通讯作者：
Dong Xia
DOI：
10.1214/21-aos2089
发表时间：
2019-04
期刊：
The Annals of Statistics
影响因子：
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作者：
Jiashun Jin;Z. Ke;Shengming Luo
通讯作者：
Jiashun Jin;Z. Ke;Shengming Luo
简单：对大型网络中的成员资料进行统计推断
DOI：
10.1111/rssb.12505
发表时间：
2022
期刊：
Journal of the Royal Statistical Society: Series B (Statistical Methodology
影响因子：
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作者：
Fan, Jianqing;Fan, Yingying;Han, Xiao;Lv, Jinchi
通讯作者：
Lv, Jinchi