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Many complex engineering systems admit bidirectional and linear couplingsbetween their agents. Blind and passive methods to identify such influencepathways/couplings from data are central to many applications. However,dynamically related data-streams originating at different sources are prone tocorruption caused by asynchronous time-stamps of different streams, packetdrops and noise. Such imperfect information may be present in the entireobservation period, and hence not detected by change-detection algorithms thatrequire an initial clean observation period. Prior work has shown that spuriouslinks are inferred in the graph structure due to the corrupted data-streams,which prevents consistent learning. In this article, we provide a novelapproach to detect the location of corrupt agents as well as present analgorithm to learn the structure of radial dynamical systems despite corrupteddata streams. In particular, we show that our approach provably learns the trueradial structure if the unknown corrupted nodes are at least three hops awayfrom each other. Our theoretical results are further validated in testdynamical network.

Topology Learning in Radial Dynamical Systems with Unreliable Data