Eigenvector Centrality is a measure used in network analysis to determine the influence of a node within a graph. Unlike simpler measures like degree centrality, which counts direct connections, eigenvector centrality considers not just the number of connections a node has, but also the quality and influence of those connections. A node connected to highly influential nodes will have a higher centrality score. This concept is often applied in various fields, including social network analysis, Google's PageRank algorithm, and biological networks. By identifying key nodes, researchers can better understand the structure and dynamics of complex systems.