  In order to extract hidden joint information from two possibly uncorrelated
time-series, we explored the measures of network science. Alongside common
methods in time-series analysis of the economic markets, mapping the joint
structure of two time-series onto a network provides insight into hidden
aspects embedded in the couplings. We discretize the amplitude of two
time-series and investigate relative simultaneous locations of those
amplitudes. Each segment of a discretized amplitude is considered as a node.
The simultaneity of the amplitudes of the two time-series is considered as the
edges in the network. The frequency of occurrences forms the weighted edges. In
order to extract information, we need to measure that to what extent the
coupling deviates from the coupling of two uncoupled series. Also, we need to
measure that to what extent the couplings inherit their characteristics from a
Gaussian distribution or a non-Gaussian distribution. We mapped the network
from two surrogate time-series. The results show that the couplings of markets
possess some features which diverge from the same features of the network
mapped from white noise, and from the network mapped from two surrogate
time-series. These deviations prove that there exist joint information and
cross-correlation therein. By applying the network's topological and
statistical measures and the deformation ratio in the joint probability
distribution, we distinguished basic structures of cross-correlation and
coupling of cross-markets. It was discovered that even two possibly known
uncorrelated markets may possess some joint patterns with each other. Thereby,
those markets should be examined as coupled and \textit{weakly} coupled
markets.
