Question 1

The________ centrality measure does not allow for centrality values to be compared across networks
A)Eigenvector
B)Katz
C)degree
D)None

Accepted Answer

The answer of The________ centrality measure does not allow for...

Question 2

Eigenvector centrality takes eigen vector of ____________
A)adjacency matrix
B)Neighbouring matrix
C)polling matrix
D)All of Above

Accepted Answer

Eigenvector centrality calculates the centrality for a node based on the centrality of its neighbors. It does this by taking the eigenvector of the adjacency matrix representing the network. The adjacency matrix is a square matrix used to represent a finite graph, with elements indicating whether pairs of vertices are adjacent or not in the graph.

Question 3

When bias term is added to the centrality values for all nodes no matter how they are situated in the network it is called_______
A)Eigenvector
B)Katz
C)degree
D)None

Accepted Answer

The Katz centrality is a measure that accounts for the total number of walks between two nodes, and it includes a bias term that adds to the centrality values for all nodes, regardless of their position in the network. This makes it distinct from measures like Eigenvector centrality, which relies on the principle that connections to high-scoring nodes contribute more to the score of a node than equal connections to low-scoring nodes, and Degree centrality, which is simply based on the number of links incident upon a node.

Question 4

__________algorithm is more effective for betweenness centrality.
A)adjacency matrix
B)Dijkstra\s
C)Neighbouring matrix
D)Brandes\

Accepted Answer

Brandes' algorithm is specifically designed to efficiently compute betweenness centrality for all nodes in a graph.

Question 5

In____________centrality, the intuition is that the more central nodes are, the more quickly they can reach other nodes.
A)Eigenvector
B)Katz
C)Closeness
D)degree

Accepted Answer

Closeness centrality is based on the idea that the centrality of a node is determined by how close it is to all other nodes in the network. This means that a node with high closeness centrality can spread information more efficiently throughout the network because it has shorter paths to all other nodes.

Quiz 8: Data Mining and Network Analysis: Key Concepts and Techniques

The________ centrality measure does not allow for centrality values to be compared across networks

Eigenvector centrality takes eigen vector of ____________

When bias term is added to the centrality values for all nodes no matter how they are situated in the network it is called_______

__________algorithm is more effective for betweenness centrality.

In____________centrality, the intuition is that the more central nodes are, the more quickly they can reach other nodes.