基于随机块模型的谱信息大型图集的概率密度估计
Probability density estimation for sets of large graphs with respect to spectral information using stochastic block models
摘要：
对于从分布$μ$采样的图值数据iid，相对于度量的选择来计算样本矩。在这项工作中，我们配备了一组图的伪度量定义的$\ell_2$规范之间的特征值的相应的邻接矩阵。我们使用这个伪度量和图值数据集的相应样本矩来推断分布$\hatμ$的参数，并将此分布解释为$μ$的近似。我们通过实验验证了复杂的分布$μ$可以很好地近似采用这种方法。
英文摘要：
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.
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