  Statistical analysis on networks has received growing attention due to demand
from various emerging applications. In dynamic networks, one of the key
interests is to model the event history of time-stamped interactions amongst
nodes. We propose to model dynamic directed communication networks via
multivariate counting processes. A pseudo partial likelihood approach is
exploited to capture the network dependence structure. Asymptotic results of
the resulting estimation are established. Numerical results are performed to
demonstrate effectiveness of our proposal.
