Detecting Weak but Hierarchically-Structured Patterns in Networks

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 networkwide 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 exi...

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Aarti Singh, Robert D. Nowak, A. Robert Calderbank
• Keywords: Activation Patterns, CORR 2010, Education, Hierarchical Dependency Graph, Weak Distributed Activation