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Our approach is motivated by results from systems biology where it has been demonstrated that the comparison of networks representing molecular pathways allows to study important modifications of functional units due to pathogenesis [24], [25].
Studying network properties in graph theory allows a comparison of networks using Ising model data and empirical fMRI data.
Some of the early distance-based methods, including the Maximal Common Subgraph (MCS) [ 10, 11] and edit distance-based [ 12] methods, focus on the global comparison of networks.
Comparison of networks obtained from different protein similarity thresholds shows that ancient genome splits contain more information about divergence of the major taxa than recent ones (fig. 4).
As a second level of comparison, we identified differences in reaction and gene content which give rise to different functional predictions of single and double gene knockout mutants using Comparison of Networks by Gene Alignment (CONGA).
A limitation of consensus networks defined by Eq. (18) (as well as by Eq. 19) and the consensus dissimilarity (22) is that the direct comparison of networks is only meaningful if the corresponding adjacencies have similar distributions.
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Figure 10 Comparison of network overhead.
Fig. 3 Comparison of network throughput.
Figure 4 The comparison of network load balance.
The comparison of network lifetime is shown in Figure 8.
Fig. 12 Comparison of available free channels number Fig. 13 Comparison of network life cycle.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com