Sharp bounds on eigenvalues via spectral embedding based on signless Laplacians
Sharp bounds on eigenvalues via spectral embedding based on signless Laplacians
复制标题
通过基于无符号拉普拉斯算子的谱嵌入对特征值进行锐界
DOI：
10.1016/j.jfa.2022.109799
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发表时间：
2023
影响因子：
1.7
通讯作者：
Wei, Zhi-Feng
中科院分区：
文献类型：
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作者：
Wei, Zhi-Feng
利用基于概率无符号拉普拉斯矩阵的谱嵌入，得到了图上转移矩阵的谱的界。作为结果，我们给出了图上简单随机游动的返回概率和一致混合时间的界。此外，本文还利用谱嵌入的方法对图邻接矩阵的谱进行了界定。我们的方法借鉴了Lyons和Oveis Gharan[13]。
Using spectral embedding based on the probabilistic signless Laplacian, we obtain bounds on the spectrum of transition matrices on graphs. As a consequence, we bound return probabilities and the uniform mixing time of simple random walk on graphs. In addition, spectral embedding is used in this article to bound the spectrum of graph adjacency matrices. Our method is adapted from Lyons and Oveis Gharan [13].