论文标题
从数据中推断网络结构
Inferring Network Structure From Data
论文作者
论文摘要
网络是许多应用程序域中基础数据的复杂模型。在大多数情况下，原始数据不是以网络的形式本地，而是源自传感器，日志，图像或其他数据。然而，各种选择将这些数据转换为网络的影响在很大程度上尚未进行。在这项工作中，我们提出了一种网络模型选择方法，该方法侧重于评估网络对不同任务的实用程序，以及选择最简约模型的效率度量。我们证明，该网络定义以多种方式对基础系统的行为进行建模至关重要。
Networks are complex models for underlying data in many application domains. In most instances, raw data is not natively in the form of a network, but derived from sensors, logs, images, or other data. Yet, the impact of the various choices in translating this data to a network have been largely unexamined. In this work, we propose a network model selection methodology that focuses on evaluating a network's utility for varying tasks, together with an efficiency measure which selects the most parsimonious model. We demonstrate that this network definition matters in several ways for modeling the behavior of the underlying system.
