Week 2

Question 1

What are the three growing random networks considered in this course?

Answer 1

Question 2

How is the degree distribution of a uniformly growing network derived?

Answer 2

Question 3

What does a Uniform Random Network represent?

Answer 3

Every period a new node is born, they start connections with m nodes. They are uniformly randomly chosen.

Question 4

What is different in the Preferential Attachment model compared to the uniform model?

Answer 4

In the uniform model connections are chosen uniformly randomly (i.e. the most popular node has the same probability of being chosen as the least popular node), in the preferential attachment model the probability of connecting with the number of links a node already has. (“The rich get richer”)

Question 5

How is the Preferential Attachment model derived?

Answer 5

Question 6

What is the idea behind the Hybrid Model?

Answer 6

Networks that have fat tails that are not linear do exist too. So it just combines a (in ([0, 1]) of the uniform and (1 - a) of the preferential model.

Question 7

How is the Hybrid Model derived?

Answer 7

Question 8

How do we estimate a in the Hybrid model?

Answer 8