  Network reconstruction of dynamical continuous-time (CT) systems is motivated
by applications in many fields. Due to experimental limitations, especially in
biology, data could be sampled at low frequencies, leading to significant
challenges in network inference. We introduce the concept of "system aliasing"
and characterize the minimal sampling frequency that allows reconstruction of
CT systems from low sampled data. A test criterion is also proposed to check
whether system aliasing is presented. With no system aliasing, the paper
provides an algorithm to reconstruct dynamic network from data in the presence
of noise. In addition, when there is system aliasing we perform studies that
add additional prior information of the system such as sparsity. This paper
opens new directions in modelling of network systems where samples have
significant costs. Such tools are essential to process the available data in
applications subject to current experimental limitations.
