  We study joint learning of network topology and a mixed opinion dynamics, in
which agents may have different update rules. Such a model captures the
diversity of real individual interactions. We propose a learning algorithm
based on multi-armed bandit algorithms to address the problem. The goal of the
algorithm is to find each agent's update rule from several candidate rules and
to learn the underlying network. At each iteration, the algorithm assumes that
each agent has one of the updated rules and then modifies network estimates to
reduce validation error. Numerical experiments show that the proposed algorithm
improves initial estimates of the network and update rules, decreases
prediction error, and performs better than other methods such as sparse linear
regression and Gaussian process regression.
