  The theory of network identification, namely identifying the interaction
topology among a known number of agents, has been widely developed for linear
agents over recent years. However, the theory for nonlinear agents remains less
extensive. We use the notion maximal equilibrium-independent passivity (MEIP)
and network optimization theory to present a network identification method for
nonlinear agents.We do so by introducing a specially designed exogenous input,
and exploiting the properties of networked MEIP systems. We then specialize on
LTI agents, showing that the method gives a distributed cubic-time algorithm
for network reconstruction in that case. We also discuss different methods of
choosing the exogenous input, and provide an example on a neural network model.
