An analyst is scoring nodes in an undirected social influence network, where a node should receive a high score when its neighbours are themselves high-scoring, and no random-walk or teleportation behaviour is required. After building the graph with cuGraph, which centrality algorithm should the analyst apply?
- APageRank
- BBetweenness centrality
- CDegree centrality
- DEigenvector centrality Correct
Why A is wrong: PageRank is a popular centrality and so is tempting, but it augments eigenvector centrality with a damping and teleportation factor designed for directed web link graphs; on a plain undirected influence network that random-walk behaviour is unnecessary and is not what the important-neighbours criterion asks for.
Why B is wrong: Betweenness centrality counts shortest-path passage frequency and is suited to finding structural bridges. It does not account for whether a node's neighbours are themselves important, so it cannot model the recursive prestige the question describes.
Why C is wrong: Degree centrality for a directed graph can be split into in-degree and out-degree counts. In-degree measures raw incoming link count but treats all source nodes as equally valuable, ignoring the recursive importance of those sources entirely.
Why D is correct: Eigenvector centrality assigns each node a score proportional to the sum of its neighbours' scores, solved iteratively as the principal eigenvector of the adjacency matrix. This directly encodes the recursive property that a node is important if its in-neighbours are important, matching the authority-scoring requirement.