G: Network Analysis

Question 1

Name six practical application areas for nework analysis:

Answer 1

Company communication flow modelling
Criminal and terrorist Network Analysis
Social Network Sites
Marketing (Targeting)
Mobility Networks
Page Rank

Question 2

Describe Adjacency Matrix

Answer 2

Matrix with vertex indications including connections between vertexes and sometimes weight (weighted adjacency matrix)

Question 3

Which centrality measure is meant? Name and explain the answer:
How many people can this person reach directly? How many potential contacts exist?

Answer 3

Degree centrality. A nodes (in/out) degree is the number of links that lead into or out of the node.

Question 4

Which centrality measure is meant? Name and explain the answer:
How likely is this person to be the most direct route between two people in the network? How likely to be involved into a path of communication.

Answer 4

Betweenness: For a given node v, calculate the number of shortest paths between nodes i and j that throught v and divide by the shortest paths between i and j

Question 5

Which centrality measure is meant? Name and explain the answer:
How fast can this person reach everyone in the network? Which person should be immunized or targeted by marketing?

Answer 5

Closeness Centrality:
Calculate the mean length of all shortest paths from a node to all other nodes in the network.

Question 6

Which centrality measure is meant? Name and explain the answer:
How well is this person connected to other well-connected people? Who is the author that is most cited by other well-cited authors?

Answer 6

Eigenvector Centrality:
A node with high eigenvector centrality is connected to other nodes with high eigenvector centralities. A node’s ev is proportional to the sum of the ev centralities of all nodes directly connected to it.

Question 7

What is a networks density?

Answer 7

… is the ratio of the number of edges in the network over the total number of possible edges between all pairs of nodes. (fünf aus sechs Beispiel)

Question 8

What is the degree of Reciprocity (=Gegenseitigkeit)?

Answer 8

The ration of the number of relations which are reciprocated over the total number of relations in the network.

Question 9

What is network clustering coefficient??

Answer 9

A node’s clustering coefficient is:
the number of closed triplets in the node’s neighbourhood///divided by
// the total number of possible triplets in the neighbourhood.

Question 10

Name three types of clustering for the three levels: 1 Node level, 2 network level, 3 network level

Answer 10

Local Clustering
Average Clustering
Overall clustering

Question 11

Name seven typical social network charakteristics:

Answer 11

Small shortest paths lengths
High clustering coefficients
High degree of homophily (Gleichgesinntheit)
Large connected components
HIgh degree of reciprocity
Skewed degree distribution
Moderate density