EVD -1

In the context of network analysis using eigen-decomposition, what does the dominant eigenvector of the adjacency matrix represent?

• The total number of connections in the network

• The most influential or central nodes in the network.

• The fastest communication path in the network.

• The weakest links in the network.

What is the primary purpose of using eigen-decomposition to detect network anomalies?

• To find the number of nodes in the network.

• To identify if a network is fully connected.

• To detect significant, unexpected changes in the network's structural properties.

• To predict future network traffic volume.

• One node is a highly centralized hub with significantly more influence than the others.

• Well-balanced and decentralized

• All nodes equally influential

• Disconnected

Eigen decomposition is used in Google’s PageRank algorithm mainly to

• Find the dominant eigenvector representing the steady-state importance of web pages
  

• Compress text documents

• Encrypt user passwords

• Improve image contrast

Eigen decomposition in face recognition is used to

• Extract principal face features

• Generate random pixel noise

• Encrypt face images

• Remove all redundant features completely

In the context of eigen-decomposition, what is the significance of the largest eigenvalue?

• Most important/dominant eigenvector

• Least important eigenvector

• Determines number of nodes

• Stability

Eigen decomposition of the covariance matrix in PCA helps to:

• Identify uncorrelated principal components

• Increase image resolution

• Encrypt pixel values

• Add Gaussian noise