1) In the context of hubs and authorities, what is the primary role of a hub in a network? a) It points to many authoritative nodes. b) It is referenced by multiple other hubs. c) It has the highest in-degree in the graph. d) It acts as a standalone influencer without dependencies.
2) Which of the following best describes the relationship between hubs and authorities in a network? a) Authorities point to hubs. b) Hubs and authorities are disconnected groups. c) Hubs reinforce the value of authorities by linking to them. d) Hubs only exist in undirected graphs.
3) What is the key idea behind the principle of repeated improvements in link analysis? a) Incrementally refining scores for nodes by iterating calculations. b) Using random assignments to simulate diffusion in the network. c) Combining the scores of hubs and authorities without iteration. d) Calculating scores only once based on initial graph structure.
4) What happens to the hub and authority scores of nodes after several iterations of the HITS algorithm? a) They converge to stable values. b) They oscillate indefinitely. c) They reset to their initial values. d) They grow without bound.
5) Which algorithm is commonly used to calculate hub and authority scores in a graph? a) HITS (Hyperlink-Induced Topic Search) b) PageRank c) Dijkstra’s Algorithm d) Breadth-First Search
6) In the HITS algorithm, how are hub and authority scores updated during each iteration? a) Hub scores are calculated based on outgoing links, and authority scores are based on incoming links. b) Hub scores are assigned randomly, and authority scores depend on the graph density. c) Hub and authority scores are not interdependent. d) Hub and authority scores are calculated independently of the adjacency matrix.
7) What ensures the convergence of the PageRank algorithm during iterative updates? a) The graph is strongly connected, and a damping factor is used. b) All nodes are assigned equal rank initially. c) Only the top-ranked nodes are considered for convergence. d) The damping factor is set to zero.
8) How does PageRank ensure conservation of rank across the graph? a) By redistributing rank proportionally across outgoing edges. b) By normalizing scores at each step of the iteration. c) By removing nodes with zero in-degree. d) By assigning higher scores to isolated nodes.
9) Why does repeated multiplication of a transition matrix in PageRank lead to convergence? a) The matrix represents a stochastic process that stabilizes over time. b) The matrix has uniform eigenvalues that dictate convergence. c) The graph contains no cycles, ensuring convergence. d) The damping factor eliminates randomness in the network.
10) In the context of PageRank, what is the significance of the dominant eigenvector of the transition matrix? a) It represents the steady-state distribution of PageRank scores. b) It determines the degree distribution of nodes in the graph. c) It is used to calculate the number of edges in the graph. d) It is irrelevant for networks with isolated nodes. View Answer
