论文标题
特征向量中心性排名的盲目推断
Blind Inference of Eigenvector Centrality Rankings
论文作者
论文摘要
我们考虑仅从节点的数据中估算网络特征向量中心性的问题，而没有有关网络拓扑的信息。利用图形过滤器到建模网络过程的多功能性，在节点上支持的数据被建模为通过应用于白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理方法来简化中心性排名的下游任务，而是直接从图形信号中推断出图的中心性结构。为此，我们提出了两种简单的算法，用于对一组未观察到的边缘连接的节点进行排名。我们为这些算法得出了渐近和非反应保证，揭示了确定手头任务复杂性的关键特征。最后，我们说明了在合成和现实世界数据集上提出的算法的行为。
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.
