  This paper studies the problem of recursively estimating the weighted
adjacency matrix of a network out of a temporal sequence of binary-valued
observations. The observation sequence is generated from nonlinear networked
dynamics in which agents exchange and display binary outputs. Sufficient
conditions are given to ensure stability of the observation sequence and
identifiability of the system parameters. It is shown that stability and
identifiability can be guaranteed under the assumption of independent standard
Gaussian disturbances. Via a maximum likelihood approach, the estimation
problem is transformed into an optimization problem, and it is verified that
its solution is the true parameter vector under the independent standard
Gaussian assumption. A recursive algorithm for the estimation problem is then
proposed based on stochastic approximation techniques. Its strong consistency
is established and convergence rate analyzed. Finally, numerical simulations
are conducted to illustrate the results and to show that the proposed algorithm
is insensitive to small unmodeled factors.

Systems and Control;Systems and Control