Corruption Detection in Networks of Bi-directional Dynamical Systems

Modeling complex networked systems as graphs is prevalent, with nodes representing the agents and the links describing a notion of dynamic coupling between them. Passive methods to identify such influence pathways from data are central to many applications. However, dynamically related data-streams originating at different sources are prone to corruption caused by asynchronous time-stamps of different streams, packet drops and noise. Earlier results have shown that spurious links are inferred in the graph structure identified using corrupt data-streams. In this article, we provide a novel approach to detect the location of corrupt agents in the network solely by observing the inferred directed graph. Here, the generative system that yields the data admits bidirectionally coupled nonlinear dynamic influences between agents. A simple, but novel and effective approach, using graph theory tools is presented to arrive at the results.