Many mobile network nodes (MNNs) in public transport move together as a large-scale mobile network.
On public methods of transportation, such as taxies, trains, buses, and airplanes, many mobile network nodes (MNNs) move together as a large-scale vehicular network.
At the ends of the lines are the many networks and nodes that connect to form the internet.
While the DEPN approach requires many perturbations, like knock-downs, but only few time points, which are regarded as independent measurements, ddepn is designed for longer time series without the necessity of many or all network nodes being perturbed.
In many networks most node pairs are not connected, but it is still meaningful to consider how nodes that are not directly connected relate to each other.
Since in many wireless networks nodes can come and go in an arbitrary manner, there must be an inherent capability of inter-node communication between every pair of nodes.
Over the past decades, the stochastic jumping neural network (SJNN) has been widely investigated due to random changes in the interconnections of dynamic network nodes, and many works have been devoted to the study of SJNN [6 8].
Importantly, many of these high-scored network nodes are not differentially expressed in the microarray data set, but were inferred by the algorithm from the differentially expressed gene pattern and the analysis of network connectivity.
Mechanistic investigation of highly scored network nodes shows that many of them play key roles in pathways for inflammation, cell cycle, and cell adhesion – all highly associated with the pathogenesis of psoriasis.
