论文标题
网络采样：估计光谱中心度度量及其在评估其他相关网络措施中的影响
Sampling on networks: estimating spectral centrality measures and their impact in evaluating other relevant network measures
论文作者
论文摘要
我们对抽样如何影响几种相关网络测量的估计值进行了广泛的分析。 特别是，我们专注于如何优化采样策略以恢复特定的光谱中心度量度影响其他拓扑数量。我们的目标一方面是扩展对TCEC [Ruggeri2019]行为的分析，这是一种理论上的特征向量核心估计方法。 另一方面，要更广泛地证明采样如何影响相关网络属性（如中心性测量）与旨在优化，社区结构和节点属性分布的相关网络属性的估计。 最后，我们将TCEC背后的理论框架适应了Pagerank中心性的情况，并提出了一种旨在优化其估计的抽样算法。我们表明，尽管可以适当地适当地适当地介绍理论推导，但所得的算法遭受了高计算复杂性，与特征向量中心性案例相比，需要进一步近似。
We perform an extensive analysis of how sampling impacts the estimate of several relevant network measures. In particular, we focus on how a sampling strategy optimized to recover a particular spectral centrality measure impacts other topological quantities. Our goal is on one hand to extend the analysis of the behavior of TCEC [Ruggeri2019], a theoretically-grounded sampling method for eigenvector centrality estimation. On the other hand, to demonstrate more broadly how sampling can impact the estimation of relevant network properties like centrality measures different than the one aimed at optimizing, community structure and node attribute distribution. Finally, we adapt the theoretical framework behind TCEC for the case of PageRank centrality and propose a sampling algorithm aimed at optimizing its estimation. We show that, while the theoretical derivation can be suitably adapted to cover this case, the resulting algorithm suffers of a high computational complexity that requires further approximations compared to the eigenvector centrality case.
