Eigenvector Centrality

Eigenvector centrality is a sophisticated metric that measures the influence of a node in a network by considering not just the number of connections, but also the influence of the nodes to which it is connected. In a financial network, a node is highly central if it is connected to other highly influential nodes.

This metric captures the "prestige" of a participant, reflecting their role as a key hub that interacts with other critical players. It is particularly useful for identifying the most important entities in a system, such as major liquidity providers or institutional custodians.

Unlike simple degree centrality, it accounts for the quality of connections, providing a more nuanced view of power dynamics. Eigenvector centrality is a core component of many network-based risk models, as it helps identify nodes that, if impacted, would have the greatest cascading effect on the network.

It is a vital tool for mapping the true structure of market influence.