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Each edge in the graph represents the information about conditional dependency between two connected nodes.
path length gives the expected distance between two connected nodes.
In addition, based on the output coupling, the amount of coupling variables between two connected nodes is flexible, which can save a lot of channel resources, simplify the network topology and has more significant meanings in engineering applications.
Unlike the nonobserver-based dynamical networks, where the coupling between two connected nodes is defined by an inner coupling matrix and full state coupling is typically needed, in this paper, smaller amount of coupling variables or even only a scalar output signal of each node is needed to synchronize the network.
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The Spearman correlation coefficients between two causally connected nodes remained high in both short and long sampling schemes.
In this model, each sub-task is represented as a node4 and an edge connecting two nodes represents a data/precedence dependency between the connected nodes.
This paper traces a way of generalization of the classical truss theory: in addition to the kinematic constraint expressing the distance between two nodes connected by a bar element, other similar constraints involving three and four nodes are introduced.
The edges between genes (nodes) indicate connectivity; wider lines indicate stronger connectivity and are indicative of greater similarity in cell cycle expression profile between the two connected genes.
Entities may flow along both directions between two nodes connected by an undirected edge [10, 11, 13].
Directed edges are also called arcs; entities may flow along only one direction between two nodes connected by an arc.
In particular, the reachability between two nodes connected by a single hop can be conventionally characterized by the packet error rate (PER) of the channel.
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