网络中弱层次模式的检测
Detecting Weak but Hierarchically-Structured Patterns in Networks
摘要：
检测网络中弱分布式激活模式的能力对几种应用至关重要，例如识别互联网中异常活动或初期拥塞的开始，或传感器网络传播的生物化学的微弱痕迹。这是一个具有挑战性的问题，因为弱分布式模式在每个节点的统计数据以及全球网络范围的聚合中是不可见的。大多数先前的工作考虑的情况下，每个节点的激活/非激活是统计独立的，但这是不现实的，在许多问题。在本文中，我们认为结构化的模式所产生的统计依赖的激活过程。我们的贡献是三方面的。首先，我们提出了一个稀疏化变换，简洁地表示结构化的激活模式，符合层次依赖图。其次，我们建立了建议的变换，便于检测非常弱的激活模式，不能用现有的方法检测。第三，我们表明，结构的层次依赖图管理的激活过程，因此网络变换，可以从很少（对数网络大小）独立的网络活动的快照。
英文摘要：
The ability to detect weak distributed activation patterns in networks is critical to several applications, such as identifying the onset of anomalous activity or incipient congestion in the Internet, or faint traces of a biochemical spread by a sensor network. This is a challenging problem since weak distributed patterns can be invisible in per node statistics as well as a global network-wide aggregate. Most prior work considers situations in which the activation/non-activation of each node is statistically independent, but this is unrealistic in many problems. In this paper, we consider structured patterns arising from statistical dependencies in the activation process. Our contributions are three-fold. First, we propose a sparsifying transform that succinctly represents structured activation patterns that conform to a hierarchical dependency graph. Second, we establish that the proposed transform facilitates detection of very weak activation patterns that cannot be detected with existing methods. Third, we show that the structure of the hierarchical dependency graph governing the activation process, and hence the network transform, can be learnt from very few (logarithmic in network size) independent snapshots of network activity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
