For the sake of extracting hidden mutual and coupled information from possibly uncoupled time-series, we explored the profound measures of network science on time-series . Alongside common methods in time-series analysis of coupling between financial and economic markets, mapping coupled time-series onto networks is an outstanding measure to provide insight into hidden aspects embedded in couplings intrinsically. In this manner, we discretize the amplitude of coupled time-series and investigate relative simultaneous locations of the corresponding amplitudes(nodes). The transmissions between simultaneous amplitudes are clarified by edges in the network. In this sense, by segmenting magnitudes, the scaling features, volatilities' size and also the direction of the coupled amplitudes can be described. The frequency of occurrences of the coupled amplitudes is illustrated by the weighted edges, that is to say, some coupled amplitudes in the time-series can be identified as communities in the network . The results show that despite apparently uncoupled joint probabilities, the couplings possess some aspects which diverge from random Gaussian noise. Thereby, with the aid of the network's topological and statistical measurements , we distinguished basic structures of coupling of cross-market networks. Meanwhile, it was discovered that even two possibly known uncoupled markets may possess coupled patterns with each other. Thereby, those markets should be examined as coupled and weakly coupled markets! \textit{ 
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.
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on time-series
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In this sense, by segmenting magnitudes, the scaling features, volatilities' size and also the direction of the coupled amplitudes can be described.
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of the coupled amplitudes is illustrated by the weighted edges, that is to say, some coupled amplitudes in the
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despite apparently uncoupled joint probabilities, the couplings possess some aspects
the couplings of markets possess some features
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random Gaussian noise. Thereby, with the aid of
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measurements
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coupling of cross-market networks. Meanwhile, it
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