  We design the first fully-distributed algorithm for generalized Nash
equilibrium seeking in aggregative games on a time-varying communication
network, under partial-decision information, i.e., the agents have no direct
access to the aggregate decision. The algorithm is derived by integrating
dynamic tracking into a projected pseudo-gradient algorithm. The convergence
analysis relies on the framework of monotone operator splitting and the
Krasnosel'skii-Mann fixed-point iteration with errors.
