Blind Inference of Eigenvector Centrality Rankings
Blind Inference of Eigenvector Centrality Rankings
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DOI：
10.1109/tsp.2021.3093765
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发表时间：
2020-08
影响因子：
5.4
通讯作者：
T. Roddenberry;Santiago Segarra
中科院分区：
文献类型：
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作者：
T. Roddenberry;Santiago Segarra
我们考虑的问题，估计网络的特征向量中心只从节点上的数据，没有关于网络拓扑结构的信息。利用图形滤波器的多功能性来对网络过程进行建模，节点上支持的数据被建模为通过应用于白色噪声的图形滤波器的输出获得的图形信号。我们试图通过绕过网络拓扑推断方法来简化中心性排名的下游任务，而是直接从图信号推断图的中心性结构。为此，我们提出了两个简单的算法排名一组节点连接的一组未观察到的边缘。我们推导出这些算法的渐进和非渐进保证，揭示了决定手头任务复杂性的关键特征。最后，我们说明了所提出的算法在合成和真实世界的数据集上的行为。
We consider the problem of estimating a network's eigenvector centrality only from data on the nodes, with no information about network topology. Leveraging the versatility of graph filters to model network processes, data supported on the nodes is modeled as a graph signal obtained via the output of a graph filter applied to white noise. We seek to simplify the downstream task of centrality ranking by bypassing network topology inference methods and, instead, inferring the centrality structure of the graph directly from the graph signals. To this end, we propose two simple algorithms for ranking a set of nodes connected by an unobserved set of edges. We derive asymptotic and non-asymptotic guarantees for these algorithms, revealing key features that determine the complexity of the task at hand. Finally, we illustrate the behavior of the proposed algorithms on synthetic and real-world datasets.