Final answer:
High betweenness centrality is important in network analysis for detecting community structure as it identifies nodes that act as bridges between communities, indicating their potential influence on information flow and network connectivity.
Step-by-step explanation:
High betweenness centrality is a key concept in network analysis used to identify nodes (vertices) in a network that serve as bridges between different parts of the network. This metric is crucial for detecting community structure because nodes with high betweenness centrality are often points of connection between distinct communities, suggesting that they have a significant role in the flow of information or interactions within the network. By measuring betweenness centrality, one can infer the potential for nodes to influence the network and identify choke points that, if removed, could disrupt the overall connectivity of the network, making it a valuable tool for understanding complex systems and optimizing network architecture.
Specifically, betweenness centrality measures the number of shortest paths going through a node. It quantifies the frequency at which a node acts as a bridge along the shortest path between two other nodes. This is important in community detection because it can reveal how clusters within a network are interconnected. Nodes with high betweenness centrality have the potential to control the flow of information within the network and can be points where the separation between communities is most pronounced.