论文标题
使用随机块模型相对于光谱信息的概率密度估计
Probability density estimation for sets of large graphs with respect to spectral information using stochastic block models
论文作者
论文摘要
对于从分布$μ$采样的图值数据，根据选择度量计算样品矩。在这项工作中，我们为图表集装备了由相应邻接矩阵的特征值之间的$ \ ell_2 $规范定义的伪金属。我们使用此伪度量标准和图值数据集的各个样品矩来推断分布$ \hatμ$的参数，并将此分布解释为近似值$μ$。我们通过实验验证复杂的分布$μ$可以很好地估计使用这种方法。
For graph-valued data sampled iid from a distribution $μ$, the sample moments are computed with respect to a choice of metric. In this work, we equip the set of graphs with the pseudo-metric defined by the $\ell_2$ norm between the eigenvalues of the respective adjacency matrices. We use this pseudo metric and the respective sample moments of a graph valued data set to infer the parameters of a distribution $\hatμ$ and interpret this distribution as an approximation of $μ$. We verify experimentally that complex distributions $μ$ can be approximated well taking this approach.
