An analyst scores nodes in a directed citation network with cuGraph, where a paper should be ranked highly when it is cited by other highly ranked papers, and where the random-walk model must include a teleportation (damping) term so that papers with no outgoing citations do not trap the walk and so that disconnected components still receive a non-zero score. Which centrality algorithm matches these requirements?
- ADegree centrality, because counting the number of incoming citations directly measures how often a paper is referenced and needs no iterative random-walk model.
- BEigenvector centrality, because it ranks a node by the eigenvector of the adjacency matrix and so accounts for the quality of the nodes pointing to it.
- CPageRank, because it models a random walker with a damping factor whose teleportation term redistributes score from dangling nodes and guarantees every node a non-zero rank. Correct
- DBetweenness centrality, because it measures how many shortest paths pass through a node and therefore captures the influence a paper exerts across the citation network.
Why A is wrong: Tempting because in-degree does count citations, but it weights every citing paper equally and has no damping or teleportation term, so it cannot satisfy the requirement that citations from highly ranked papers count more or that dangling nodes be handled by a random walk.
Why B is wrong: Tempting because eigenvector centrality also rewards being linked to by important nodes, but it has no teleportation term, can fail to converge on directed graphs with dangling nodes, and may assign zero score to nodes outside the dominant component, which the scenario explicitly rules out.
Why C is correct: Correct: PageRank's recursive definition rewards being cited by high-scoring nodes, and its damping or teleportation parameter is exactly what handles dangling nodes and disconnected components, which is what the scenario specifies.
Why D is wrong: Tempting because betweenness is a well-known importance measure, but it quantifies bridging on shortest paths rather than being cited by important nodes, and it uses no random-walk or damping model at all, so it does not fit the stated requirement.