NPTEL Social Network Analysis Week 3 Assignment Answers 2024

1. A network is said to follow small-world property if:

Average path length α (log(network size))-1
Average path length α (exp(network size))-1
Average path length α log(network size)
Average path length α exp(network size)

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2. A giant component appears in a random network when the average degree of the network is:

greater than or equal to 0.5
less than or equal to 0.5
less than or equal to 1
greater than or equal to 1

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3. Erdos-Renyi Random Network

High clustering coefficient
Small world property
Degree distribution following power law

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4. Watts-Strogatz Model

Small world property
Degree distribution following power law
High clustering coefficient

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5. Barabasi-Albert Model

Small world property
High clustering coefficient
Degree distribution following power law

Answer :-

6. The clustering coefficient of a node is not (multiple-choice):

Independent of p
Dependent on p
Dependent on the node’s degree
Independent of the node’s degree

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7. The principle of preferential attachment can also be thought of as:

Rich attracts rich
Poor gets richer
Rich gets richer
Rich gets poor

Answer :-

8. What are the different regimes in the plot below that characterize a random network?

I: Supercritical regime; II: Critical point; III: Subcritical regime; IV: Connected regime
I: Connected regime; II: Critical point; III: Supercritical regime; IV: Subcritical regime
I: Subcritical regime; II: Critical point; III: Connected regime; IV: Supercritical regime
I: Subcritical regime; II: Critical point; III: Supercritical regime; IV: Connected regime

Answer :-

9. What characteristic of the Barabási-Albert (BA) model distinguishes it from other random graph models?

Each new node connects to a fixed number of existing nodes based on the node degree.
All nodes have the same number of links.
New nodes are added at random without preference.
Nodes can be removed and added back into the system.

Answer :-

10. Based on the following formula for the clustering coefficient of a Watts-Strogatz model, determine the minimum and maximum values of CCWS, given that each node is connected to k/2 forward and k/2 backward nodes.

CC_WS = 3(k-2)/4(k-1)