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In complex networks, interactions between multiple agents give rise to anarray of intricate global dynamics, ranging from synchronization to clusterformations. Decoding the connectivity structure as well as the types ofinteractions from measurement data is the first step toward understanding theseintriguing behaviors. In this paper, we present a bilinear optimizationframework to infer both the connectivity and interaction functions ofoscillator networks with the identical class of coupling functions. We thenpropose an iterative algorithm to solve the resulting bilinear problem andillustrate its convergence. We validate our approach on both simulated andnoisy experimental datasets, where we demonstrate its effectiveness comparedwith existing approaches.

An Iterative Approach to Data-Driven Inference for Decoding Oscillator Network Structures