In network analysis, which centrality measure best identifies influential actors who are connected to other influential actors?

• A. Betweenness centrality
• B. Degree centrality
• C. Eigenvector centrality
• D. Closeness centrality

Answer: (C)

The idea being tested is that influence in a network comes from who you are connected to, not just how many connections you have. Eigenvector centrality captures this by assigning a node a high score if it is tied to other high-scoring nodes. It’s computed from the principal eigenvector of the adjacency matrix, so a node’s value depends on the scores of its neighbors. In practical terms, an actor connected to several influential actors gains more influence themselves, because their connections lead to those same influential networks.

This differs from simply counting connections, which is what degree centrality does. You could have many ties to ordinary actors but still not sit near the core of influence. Betweenness centrality finds nodes that bridge different parts of the network, highlighting bottlenecks and brokers rather than the quality of neighbors. Closeness centrality looks at how near a node is to everyone else, based on path lengths, without considering the influence level of those paths. Eigenvector centrality uniquely emphasizes being linked to other influential actors, making it the best fit for identifying those who are connected to other influential figures.