  We estimate the general influence functions for spatio-temporal Hawkes
processes using a tensor recovery approach by formulating the location
dependent influence function that captures the influence of historical events
as a tensor kernel. We assume a low-rank structure for the tensor kernel and
cast the estimation problem as a convex optimization problem using the Fourier
transformed nuclear norm (TNN). We provide theoretical performance guarantees
for our approach and present an algorithm to solve the optimization problem.
Moreover, we demonstrate the efficiency of our estimation with numerical
simulations.
