Network analysis explores connections and relationships within various systems, from social circles to biological pathways. Within these networks, some nodes hold greater significance or exert more influence. Identifying these influential nodes is a central aim of network analysis. Betweenness centrality quantifies this importance, highlighting nodes that serve as intermediaries in the flow of information or resources.
What is Betweenness Centrality?
Betweenness centrality quantifies how much a node acts as a bridge or connector within a network. It measures a node’s control over the flow of information or traffic between other nodes. Nodes with high betweenness centrality are positioned on many of the shortest communication paths between other pairs of nodes. This positioning allows them to act as intermediaries, controlling the information or resources that pass through them.
For example, a busy intersection in a city’s road network has high betweenness centrality because most cars traveling between different parts of the city must pass through it. Similarly, a person connecting two otherwise separate groups in a communication network acts as a “middleman” or “broker” with high betweenness. Such nodes are important bridges between different network parts, making them important for connectivity and flow.
How it is Measured
Measuring betweenness centrality involves identifying the most efficient routes between all possible pairs of nodes. These “shortest paths” or “geodesic paths” are defined as the path with the fewest connections or lowest cumulative weight. Once these shortest paths are determined for every pair of nodes, the calculation counts how many of these paths pass through a particular node.
For each node, its betweenness centrality score is derived by summing the proportion of all shortest paths between any two other nodes that include it. If multiple shortest paths exist, each path contributes fractionally to the node’s score. This method quantifies a node’s role as an intermediary, indicating its capacity to mediate connections or control information flow. The greater the number of shortest paths passing through a node, the higher its betweenness centrality value.
Why it Matters in Networks
Nodes possessing high betweenness centrality hold important implications for a network’s function and resilience. Such nodes can exert significant influence because they mediate the flow of information, resources, or communication between various parts of the network. Their position allows them to control or filter what passes through them, potentially acting as gatekeepers.
These highly central nodes are also important for network connectivity. If a node with high betweenness centrality is removed or fails, it can significantly disrupt communication or transport throughout the network, potentially fragmenting it. This vulnerability makes them potential bottlenecks where congestion or breakdown can occur. Understanding which nodes have high betweenness centrality helps identify points of influence, potential control, and structural weaknesses.
Where it is Applied
Betweenness centrality finds practical application across various fields, providing insights into diverse networks.
Social Networks
In social networks, it helps identify key individuals who connect different social circles or groups. These individuals often serve as opinion leaders or important conduits for information dissemination, making them targets for marketing campaigns or public health initiatives.
Transportation Networks
In transportation networks, betweenness centrality can pinpoint important intersections, roads, or airports essential for efficient travel. Disruptions at these high-betweenness locations can cause widespread delays and congestion.
Biological Networks
For biological networks, such as protein-protein interaction networks, it can highlight central proteins that mediate interactions between various cellular pathways, suggesting their importance for biological functions.
Computer Networks
It also aids in analyzing computer networks to identify important routers or servers that handle a large volume of data traffic, enabling better network design and security measures.