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As a consequence, many shortest paths are discarded.
Large betweenness centrality of a vertex indicates that many shortest paths between couples of other vertices pass through that node.
As a consequence, many shortest paths in terms of the Euclidean distances are discarded between any pair of sensor nodes.
More specifically, node betweenness centrality indicates a particular node's centrality in graph, which refers to how many shortest paths from all nodes to all others pass through that particular node [79].
A node that appears in many shortest paths has high betweenness.
Node C in Figure 5 has a high betweenness given that many shortest paths joining nodes pass through it.
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Based on their position in the network, some nodes can interact more easily with other nodes, or are on many short paths between other pairs of nodes.
For global methods, these metabolites introduce many short paths through the network.
However, there are many shorter routing paths, e.g., MNN1 → MR3 → MR7 → MR5 → MNN2 as shown in Figure 3b.
To be more specific, BC is based on the concept that a street is central if it is included in many of the shortest paths linking pairs of nodes (street intersections) in a street network.
Motif fingerprints capture the statistics of local connection patterns, while measures of centrality identify regions that lie on many of the shortest paths between parts of the network.
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