Social Networks Week 1 NPTEL Assignment Answers 2025

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✅ Subject: Social Networks
📅 Week: 1
🎯 Session: NPTEL 2025 July-October
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NPTEL Social Networks Week 1 Assignment Answers 2025

1. Which property of the peripheral nodes likely contributed to rapid spread?

• High degree within their own group
• Strong ties within small clusters
• Weak ties across different clusters
• Low betweenness centrality

Answer : For Answers Click Here 

2. What characteristics of the network made it suitable for analyzing information spread?

• Modeled using directed graphs
• Edges represent real communication events
• Nodes change over time
• Network captures tie strength and type

Answer :

3. Why did the officials’ initial assumption (news spreads from hubs) fail?

• Central nodes had no social media
• Strong ties inhibit new information
• Peripheral nodes had more information
• Central nodes avoided communication

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4. Which measures could help identify the key spreaders?

• Closeness centrality
• Eigenvector centrality
• Degree centrality only
• Betweenness centrality

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5. If the network is very sparse but has a few long bridges, what does this imply?

• Faster diffusion through hubs
• Delayed spread due to cluster boundaries
• High modularity
• Opportunity for rapid cross-cluster spread

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6. How could the government improve its warning system using network insight?

• Send alerts only to cluster heads
• Use peripheral weak-tie nodes to seed warnings
• Improve communication with high-betweenness nodes
• Track frequency of communication instead of just structure

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7. What causes a low-quality course to be ranked high in PageRank?

• It has many incoming links from highly ranked courses
• It has a high out-degree
• It is recently added
• It has the longest video lectures

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8. Which factors could help adjust the PageRank algorithm to improve fairness?

• Penalize nodes with too many incoming links
• Use personalization vectors for user preferences
• Include course ratings and completions
• Apply damping factor to avoid cycles

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9. If a course has no outbound links, how is it treated in classic PageRank?

• It’s ignored
• Its rank is distributed uniformly across the graph
• It becomes a sink and absorbs rank
• Its rank is set to zero

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10. Which of the following indicate a biased ranking system?

• Popular but poorly rated courses at the top
• Recently added quality content ranked last
• Many courses with equal PageRank values
• Unlinked courses getting high rank

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11. What does the damping factor in PageRank model?

• Probability of teleporting to any node
• Likelihood of increasing edge weights
• Quality of content on the node
• Number of outgoing links from a node

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12. To improve niche course visibility, the platform could:

• Add links from top courses to niche ones
• Give every node the same initial rank
• Modify PageRank using relevance scores
• Increase random jump probability to niche areas

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13. In a social network, which topology supports efficient local search?

• Random network
• Regular grid
• Small-world network
• Fully connected graph

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14. What features reduce average search path length in networks?

• High clustering
• Long-range shortcuts
• Low average degree
• Nodes with high degree variation

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15. Why do regular grids perform poorly for local search?

• Too many links
• Lack of randomness
• Clusters are not isolated
• Degrees are too high

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16. Which heuristics can improve local search?

• Greedy search using geographic distance
• Forwarding to neighbor with most connections
• Random forwarding
• Forwarding to neighbor closer to target in metadata

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17. What does failure of search in some cases suggest about the network?

• Nodes are disconnected
• Too many strong ties
• High modularity with few bridges
• Degree distribution is uniform

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18. What strategies could the app use to improve matching success?

• Add random links between distant users
• Use known frequent paths from history
• Remove users with low activity
• Cluster users and allow intra-cluster hops

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19. Which principle is the algorithm likely using?

• Link locality
• Homophily and triadic closure
• Preferential attachment
• Degree centrality

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20. What features commonly improve link prediction?

• Number of mutual friends
• Jaccard similarity
• Network diameter
• Common interactions

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21. If two users have many common neighbors but don’t connect, what might be the reason?

• Algorithm missed triadic closure
• Lack of profile similarity or activity
• Network diameter is too high
• Too many clusters

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22. Which scenarios likely indicate false positives in prediction?

• Many shared neighbors but no shared activity
• Same hometown, never interacted
• Friends from different cliques
• Two people liking each other’s posts regularly

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23. What does high clustering coefficient suggest?

• Dense triads, good for link prediction
• Sparsely connected components
• Nodes with high betweenness
• Degree is evenly distributed

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24. What else can improve prediction accuracy?

• Using temporal patterns of interaction
• Including profile text similarity
• Ignoring mutual friends
• Using machine learning on node features

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25. Which of the following best explains why Set B outperforms Set A in reach?

• Betweenness centrality captures influencers who bridge communities
• High degree nodes are always less active
• Random selection is statistically better
• The cascade model favors random walks

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26. What factors should the startup consider when choosing seed nodes to maximize reach?

• Community bridging potential
• Total number of followers
• Follower engagement rate
• Position in the network structure

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27. In the Independent Cascade model, what happens if a node fails to influence a neighbor?

• It retries in the next round
• The neighbor is permanently immune
• It can try again if another neighbor influences it
• The chance of influence increases next time

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28. Which of the following are limitations of using only degree centrality to select influencers?

• Ignores clustering
• Doesn’t account for overlapping audiences
• Assumes uniform influence probability
• Always targets nodes on the periphery

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29. Suppose an influencer with low degree but high betweenness is selected. Their value lies in:

• Posting at fixed times
• Creating echo chambers
• Connecting otherwise distant groups
• Having more followers than others

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30. The startup notices influencers form tight-knit clusters. To maximize diffusion, they should:

• Choose influencers all from one dense cluster
• Pick seed nodes from different clusters
• Maximize follower overlap among seeds
• Avoid seeding in isolated nodes

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NPTEL Social Networks Week 1 Assignment Answers 2025

1. Which of the following is the output of the given code segment?

l=[]

l.append (5)

l.append (”AAA”)

l.append ([2,3])

print(l)

• [5, AAA, [2, 3]]
• [5,‘AAA’,[2, 3]]
• [5,‘AAA’,2,3]
• error

Answer :- b

2. Assume you have to roll a dice with six faces. Choose the statement to simulate the same.

• random.randint(1, 6)
• random.randrange(1, 8)
• random.randint(1, 7)
• random.randrange(1,6)

Answer :- a

3. Select the code to create the given dictionary:
{1 : 2, 2 : 4, 3 : 6, 4 : 8, 5 : 10, 6 : 12, 7 : 14, 8 : 16, 9 : 18}

• d={x:x**2 for x in range(1,10)}print(d)
• d={x:x*2 for x in range(1,9)}print(d)
• d={x:x*2 for x in range(1,10)}print(d)
• d={x:x**2 for x in range(1,9)}print(d)

Answer :- c

4. What does the G(n,p) random graph model in Networkx library represent?

• A graph with n nodes and p edges
• A graph with n nodes and p is the probability of an edge between any two nodes
• A graph with n nodes and p-1 edges
• A graph with n nodes and p is the probability of a path of any length between every two nodes

Answer :- b

5. What is the output of the following code snippet?

import networkx
G = networkx.Graph( )
G.add_edges_from ([(2,1), (2,3), (4,2), (2,5)])
G.remove node ( 2 )
print(len(G.edges( )))

• 0
• 1
• 2
• 3

Answer :- a

6.

Which of the following functions is used to find the shortest path length?

• networkx.dijkstra_path_length(G,‘node a’)
• networkx.dijkstra_path_length(‘node a’,‘node b’)
• networkx.dijkstra_path_length(‘node a’,‘node b’,G)
• networkx.dijkstra_path_length(G,‘node a’,‘node b’)

Answer :- d

7. What does the Page Rank algorithm measure?

• The number of friends a person has in the social network
• The person who is more important in the network
• The person who is very frequently posting content on the social network
• The total number of connections in the network

Answer :- b

8. The code inside the ‘try’ block is monitored for any exceptions. The ‘except’ block contains the code to handle the exception, providing an alternative path for the program to continue execution. Find the output for the given code snippet:

x=[5,2,7,3,8]

try :

a=x[3]

if(a%2==0):

print(”It is an even number”)

else:

print(”It is an odd number”)

except:
print(”Element does not exist”)

• Element does not exist
• It is an even number
• It is an odd number
• error

Answer :- c

9. What is the maximum number of graphs possible from 70 nodes?

Answer :- b

10. Which algorithm or concept should be used to suggest connections on LinkedIn?

• Link Prediction
• Page Ranking
• Hits algorithm
• BFS

Answer :- a