Also, since the complex task in MNN is decomposed into several smaller and simpler ones, one can expect an overall network with a smaller complexity and CPU demanding.
To address this challenge, we developed a high-throughput reverse engineering strategy to analyze how thousands of specific perturbations (encompassing all point mutations in a central gene) impact both a specific edge (interaction to a directly connected node) and an overall network function.
Using the Newman-Girvan module detection algorithm, we detected a total of 10 modules, with an overall network modularity of 0.519.
The model was run for 40,561 km of streams in Utah, USA, and portions of surrounding states, predicting an overall network capacity of 356,294 dams at an average capacity of 8.8 dams/km.
Not only does this give us a computational means of predicting function from genomic information, but the process of building these models represents an added level of functional annotation within the context of an overall network [ 13].
These factors, further contribute towards an overall network lifetime extension under SHEAR.
The flow dynamic as an overall network performance index is largely formulated as changes in total travel time [4, 31].
However, from a network point of view, only an increased capacity in one specific layer is not sufficient to improve an overall network performance.
Being too fair leads then the network to follow the policy of highly loaded BSs, thus providing an overall network utility which is almost null.
However, SDN-based load balancing mechanism uses an overall network view when selecting the target cell, which decreases handover times and improves system performance.
