## 3 Analysis Results
### Connectivity Characteristics of Networks  
In this section, we characterize the dynamics of SIT and DIT networks in terms of connectivity (degree) and clustering coefficients (cliquishness). Further, in order to minimize the effect of low connectivity on the cliquishness of a network, we use the composite map of degrees and clustering coefficients. Also, to test the randomness of SIT and DIT networks, we used 100 ensembles of randomized networks (dotted line in Figure 9A), which are generated by randomizing the links of corresponding networks.  
SIT networks show different behaviors in the upper and lower layers in terms of clustering coefficients, as shown in Figure 9. There is no difference between the inner and outer centers in the lower layer (Line with square in Figure 9A), while a dramatic change is observed in the upper layer (Line with circle in Figure 9A). However, caution should be taken in interpreting these behaviors in terms of interactions between SITs and the eddy, as the connectivity in the upper layer is too low. A clear observation is that there is little interaction in the lower layer (Figure 9A). From the composite map of the mean degree and mean clustering coefficients in Figure 9B, the lower layer seems to be irrespective of the horizontal domains (inner and outer centers; see color of square in Figure 9B), while the upper layer exhibits a clear differentiation between the inner and outer centers (except for one green node); the node, corresponding to Day 8 network, seems to be an abnormal behavior, probably due to sensitive incoherence feature of SITs [21].  
DIT networks show more clearly a differentiation between the inner and outer centers in the upper layer in terms of mean clustering coefficients (Figure 10A). Considering that DITs are the low-mode internal tides [4,5] originating solely from the Luzon strait [44], this behavior supports that there is a strong interaction between DITs and the eddy in the inner center. As in SIT networks, the mean clustering coefficients of DIT networks are irrespective of the horizontal domain in the lower layer. In the degree-clustering coefficient composite map (Figure 10B), there is a clear differentiation in the lower layer--a high degree and high clustering coefficient in the inner center compared to those in the outer center. This  
Figure 9: A chart of mean clustering coefficients (**A**) and a composite map of mean degree and mean clustering coefficient (**B**) for SIT networks.  
behavior can be due to a weakening effect of the interaction in the outer center, resulting in a loose cliquish structure in the lower layer due to strong inter-layer connectivity. In the upper layer (Circle in Figure 10B), a clear differentiation is observed; DIT networks in the outer center have a high mean degree and clustering coefficient, while they have low values in the inner center.  
Comparing the two network metrics, the clustering coefficient appears to be more reliable for assessing the structural variability of wavelet-coherence-based networks constructed from oscillatory time series. However, a composite approach with degree and clustering coefficients could be more appropriate.