Social Networks Week 1 Nptel Assignment Answers
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Nptel Social Networks Week 1 Assignment Answers (Jan-Apr 2026)
Que1. The communication system described is most appropriately represented as:
a) A table of contacts
b) A graph of users and connections
c) A loop-based structure
d) A sorted list
Que2. The main reason some students never received the notice was that:
a) The message was sent too late
b) The coordinators did not forward it
c) There were no communication paths to them
d) The system deleted messages
Que3. Ignoring self-messaging and duplicate connections makes the structure:
a) Random
b) Directed
c) Simple
d) Cyclic
Que4. Tracing the spread of the message step by step from the hostel office is best described as:
a) Sorting users
b) Searching through connections
c) Counting messages
d) Reversing data
Que5. If every student could directly message every other student, the notice would:
a) Still spread slowly
b) Reach only coordinators
c) Reach everyone immediately
d) Fail due to overload
Que6. Which Python structure is most suitable for storing hourly data in sequence?
a) Set
b) Dictionary
c) List
d) Tuple
Que7. Reversing the list helped the coordinator mainly to:
a) Change the values
b) Analyze recent activity first
c) Increase the data size
d) Remove older data
Que8. If three numbers between 0 and 1 are added, which outcome is impossible?
a) 0.9
b) 1.6
c) 2.7
d) 3.8
Que9. The random number experiment showed that randomness:
a) Has no limits
b) Always gives large values
c) Produces varying results within bounds
d) Removes all patterns
Que10. Creating more connections between users mainly helps to:
a) Reduce data storage
b) Improve message reach
c) Decrease user count
d) Control randomness
Que11. A system with 500 guards and 499 links where everyone is reachable suggests that:
a) The system is inefficient
b) The system has the minimum required connections
c) The system is overloaded
d) The system is disconnected
Que12. Passing alerts from one guard to another step by step represents:
a) Searching
b) Sorting
c) Printing
d) Deleting
Que13. Adding extra random links mainly resulted in:
a) Faster alerts
b) Fewer message paths
c) Repeated alerts
d) Message loss
Que14. Communication that works equally in both directions means the system is:
a) One-way
b) Two-way
c) Circular
d) Isolated
Que15. This case study mainly shows that increasing connections always:
a) Improves performance
b) Simplifies communication
c) Helps only up to a point
d) Eliminates confusion
Nptel Social Networks Week 1 Assignment Answers (July-Dec 2025)
Question 1. Which property of the peripheral nodes likely contributed to rapid spread?
a) High degree within their own group
b) Strong ties within small clusters
c) Weak ties across different clusters
d) Low betweenness centrality
Question 2. What characteristics of the network made it suitable for analyzing information spread?
a) Modeled using directed graphs
b) Edges represent real communication events
c) Nodes change over time
d) Network captures tie strength and type
Question 3. Why did the officials’ initial assumption (news spreads from hubs) fail?
a) Central nodes had no social media
b) Strong ties inhibit new information
c) Peripheral nodes had more information
d) Central nodes avoided communication
Question 4. Which measures could help identify the key spreaders?
a) Closeness centrality
b) Eigenvector centrality
c) Degree centrality only
d) Betweenness centrality
Question 5. If the network is very sparse but has a few long bridges, what does this imply?
a) Faster diffusion through hubs
b) Delayed spread due to cluster boundaries
c) High modularity
d) Opportunity for rapid cross-cluster spread
These are Nptel Social Networks Week 1 Assignment Answers
Question 6. How could the government improve its warning system using network insight?
a) Send alerts only to cluster heads
b) Use peripheral weak-tie nodes to seed warnings
c) Improve communication with high-betweenness nodes
d) Track frequency of communication instead of just structure
Question 7. What causes a low-quality course to be ranked high in PageRank?
a) It has many incoming links from highly ranked courses
b) It has a high out-degree
c) It is recently added
d) It has the longest video lectures
Question 8. Which factors could help adjust the PageRank algorithm to improve fairness?
a) Penalize nodes with too many incoming links
b) Use personalization vectors for user preferences
c) Include course ratings and completions
d) Apply damping factor to avoid cycles
Question 9. If a course has no outbound links, how is it treated in classic PageRank?
a) It’s ignored
b) Its rank is distributed uniformly across the graph
c) It becomes a sink and absorbs rank
d) Its rank is set to zero
These are Nptel Social Networks Week 1 Assignment Answers
Question 10. Which of the following indicate a biased ranking system?
a) Popular but poorly rated courses at the top
b) Recently added quality content ranked last
c) Many courses with equal PageRank values
d) Unlinked courses getting high rank
Question 11. What does the damping factor in PageRank model?
a) Probability of teleporting to any node
b) Likelihood of increasing edge weights
c) Quality of content on the node
d) Number of outgoing links from a node
Question 12. To improve niche course visibility, the platform could:
a) Add links from top courses to niche ones
b) Give every node the same initial rank
c) Modify PageRank using relevance scores
d) Increase random jump probability to niche areas
Question 13. In a social network, which topology supports efficient local search?
a) Random network
b) Regular grid
c) Small-world network
d) Fully connected graph
Question 14. What features reduce average search path length in networks?
a) High clustering
b) Long-range shortcuts
c) Low average degree
d) Nodes with high degree variation
Question 15. Why do regular grids perform poorly for local search?
a) Too many links
b) Lack of randomness
c) Clusters are not isolated
d) Degrees are too high
These are Nptel Social Networks Week 1 Assignment Answers
Question 16. Which heuristics can improve local search?
a) Greedy search using geographic distance
b) Forwarding to neighbor with most connections
c) Random forwarding
d) Forwarding to neighbor closer to target in metadata
Question 17. What does failure of search in some cases suggest about the network?
a) Nodes are disconnected
b) Too many strong ties
c) High modularity with few bridges
d) Degree distribution is uniform
These are Nptel Social Networks Week 1 Assignment Answers
Question 18. What strategies could the app use to improve matching success?
a) Add random links between distant users
b) Use known frequent paths from history
c) Remove users with low activity
d) Cluster users and allow intra-cluster hops
Question 19. Which principle is the algorithm likely using?
a) Link locality
b) Homophily and triadic closure
c) Preferential attachment
d) Degree centrality
Question 20. What features commonly improve link prediction?
a) Number of mutual friends
b) Jaccard similarity
c) Network diameter
d) Common interactions
Question 21. If two users have many common neighbors but don’t connect, what might be the reason?
a) Algorithm missed triadic closure
b) Lack of profile similarity or activity
c) Network diameter is too high
d) Too many clusters
Question 22. Which scenarios likely indicate false positives in prediction?
a) Many shared neighbors but no shared activity
b) Same hometown, never interacted
c) Friends from different cliques
d) Two people liking each other’s posts regularly
These are Nptel Social Networks Week 1 Assignment Answers
Question 23. What does high clustering coefficient suggest?
a) Dense triads, good for link prediction
b) Sparsely connected components
c) Nodes with high betweenness
d) Degree is evenly distributed
Question 24. What else can improve prediction accuracy?
a) Using temporal patterns of interaction
b) Including profile text similarity
c) Ignoring mutual friends
d) Using machine learning on node features
Question 25. Which of the following best explains why Set B outperforms Set A in reach?
a) Betweenness centrality captures influencers who bridge communities
b) High degree nodes are always less active
c) Random selection is statistically better
d) The cascade model favors random walks
Question 26. What factors should the startup consider when choosing seed nodes to maximize reach?
a) Community bridging potential
b) Total number of followers
c) Follower engagement rate
d) Position in the network structure
Question 27. In the Independent Cascade model, what happens if a node fails to influence a neighbor?
a) It retries in the next round
b) The neighbor is permanently immune
c) It can try again if another neighbor influences it
d) The chance of influence increases next time
Question 28. Which of the following are limitations of using only degree centrality to select influencers?
a) Ignores clustering
b) Doesn’t account for overlapping audiences
c) Assumes uniform influence probability
d) Always targets nodes on the periphery
These are Nptel Social Networks Week 1 Assignment Answers
Question 29. Suppose an influencer with low degree but high betweenness is selected. Their value lies in:
a) Posting at fixed times
b) Creating echo chambers
c) Connecting otherwise distant groups
d) Having more followers than others
Question 30. The startup notices influencers form tight-knit clusters. To maximize diffusion, they should:
a) Choose influencers all from one dense cluster
b) Pick seed nodes from different clusters
c) Maximize follower overlap among seeds
d) Avoid seeding in isolated nodes
Nptel Social Networks Week 1 Assignment Answers (Jan-Apr 2025)
Course Link: Click Here
1. Which Python code correctly computes the sum of even numbers in the list L = [3, 5, 8, 2, 6]?
- (a)
sum(x for x in L if x % 2 == 0) - (b)
sum(x for x in L if x % 2 != 0) - (c)
sum(x for x in L if x > 2) - (d)
sum(L)
2. You are given a dictionary d = {'A': 10, 'B': 20, 'C': 30}. Which Python code snippet correctly adds a new key 'D' with value 40 to the dictionary?
- (a)
d['D'] = 40 - (b)
d.add('D', 40) - (c)
d.update('D', 40) - (d)
d.update({'D': 40})
3. Given the following Python code snippet, what will be the output?
import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [1, 4, 9, 16]
plt.scatter(x, y)
plt.show()
- (a) A bar chart displaying the values in
xandy. - (b) A line graph connecting the points
(1, 1),(2, 4),(3, 9),(4, 16). - (c) A scatter plot with points
(1, 1),(2, 4),(3, 9),(4, 16). - (d) A histogram displaying the values of
xandy.
These are Nptel Social Networks Week 1 Assignment Answers
4. Using the NetworkX library, how would you create an undirected graph with three nodes (A, B, C) and two edges (A-B and B-C)?
- (a)
G = nx.Graph()
G.add_edges_from([('A', 'B'), ('B', 'C')])
- (b)
G = nx.DiGraph()
G.add_edges_from([('A', 'B'), ('B', 'C')])
- (c)
G = nx.Graph()
G.add_edges([('A', 'B'), ('B', 'C')])
- (d)
G = nx.Graph()
G.add_edges_from([('A', 'B'), ('B', 'C')], directed=True)
5. In the PageRank algorithm, what is the primary assumption regarding the link structure of the web?
- (a) Pages that are linked to more often are likely more important.
- (b) Pages that are less frequently linked are likely more important.
- (c) Pages with more content are likely more important.
- (d) Pages without links are considered equally important as those with links.
These are Nptel Social Networks Week 1 Assignment Answers
6. In a social network, you are tasked with finding the shortest path between two individuals, A and B. Which of the following algorithms would be suitable?
- (a) Dijkstra’s Algorithm
- (b) A* Search Algorithm
- (c) Breadth-First Search (BFS)
- (d) Depth-First Search (DFS)
7. Which of the following methods is commonly used for link prediction in a social network?
- (a) Collaborative Filtering
- (b) K-means Clustering
- (c) Matrix Factorization
8. In models of contagion in social networks, what does the term “threshold” specifically refer to?
- (a) The minimum number of links required to spread an infection in a network.
- (b) The fraction of neighbors a node needs to be influenced by to adopt a new behavior.
- (c) The time it takes for an infection to spread from one node to another.
- (d) The maximum number of nodes that can be infected at any given time.
9. Which centrality measure would you use to find the individuals who have the shortest average path length to all other nodes in the network?
- (a) Degree Centrality
- (b) Closeness Centrality
- (c) Betweenness Centrality
- (d) Eigenvector Centrality
These are Nptel Social Networks Week 1 Assignment Answers
10. Which of the following NetworkX functions can be used to predict potential links in a network based on node similarity indices?
- (a)
nx.closeness_centrality(G) - (b)
nx.jaccard_coefficient(G) - (c)
nx.adamic_adar_index(G) - (d)
nx.preferential_attachment(G)
Nptel Social Networks Week 1 Assignment Answers (Jul-Dec 2024)
Course Link: Click Here
Q1. Which function will you use to find the number of edges drawn in the Graph?
order( )
edges( )
edge_size( )
number_of_edges( )
Answer: number_of_edges( )
Q2. What will be the maximum number of edges for a graph with 10 vertices?
10
45
55
100
Answer: 45
Q3. What does items() function for a dictionary return?
Dictionary with all keys and values
List with tuples having keys and values
List containing all keys and values
Tuples containing all keys and values
Answer: List with tuples having keys and values
These are Nptel Social Networks Week 1 Assignment Answers
Q4. What will be the output of the given code?
x=[i for i in range(10)]
plt.plot(x,x**2,‘r=’)
plt.show ( )
Error displaying unsupported operand types for ** in plot
It plots the x with its exponential value with points and lines in the plot
It plots x with expontential value with points in the plot
It plots x with expontential value with red line in the plot
Answer: Error displaying unsupported operand types for ** in plot
Q5. What does the value ‘ys-’ as the third parameter in plot function indicate?
It helps to plot square with line connecting the points
It helps to plot yellow color square with dotted line connecting the points
It helps to plot yellow color square with line connecting the points
It helps to plot yellow color dotted line
Answer: It helps to plot yellow color square with line connecting the points
Q6. Choose the correct statement to convert a list ‘List1’ to array in numpy.
List1.numpy()
List1.numpy array()
List1=numpy.array(List1)
List1.numpy.array()
Answer: List1=numpy.array(List1)
These are Nptel Social Networks Week 1 Assignment Answers
Q7. Use networkx package, and write the statement to add the edges between two nodes (1,2) of a graph G=nx.Graph().
G.add_edge()
G.add_edge(1,2)
G.addedge(1,2)
G.AddEdge(1,2)
Answer: G.add_edge(1,2)
Q8. If you want to generate a random number from an inclusive range which function is to be used?
randrange()
randint()
random.random()
rand_Int()
Answer: randint()
These are Nptel Social Networks Week 1 Assignment Answers
Q9. If a=‘Social’, b=‘Networks’ then which of the following operation would show ’SocialNetworks’ as output?
a+b
a+”+b
a+””+b
All of the above
Answer: All of the above
These are Nptel Social Networks Week 1 Assignment Answers
Q10. In the command networkx.erdos_renyi_graph(a,b), the parameters ‘a’ and ‘b’ denote the following respectively:
Number of edges and the probability with which edges are to be placed between every pair of nodes
Number of nodes and the probability with which edges are to be placed between every pair of nodes
The probability with which edges are to be placed between every pair of nodes and Number of edges
Number of edges and Number of nodes
Answer: Number of nodes and the probability with which edges are to be placed between every pair of nodes
Nptel Social Networks Week 1 Assignment Answers (Jan-Apr 2023)
Course Link: Click Here
Q1) What would be the output for the following code?
scores=[78,77,45]
scores.append(90)
scores.append(98)
scores.reverse()
print(scores)
a. [98, 90, 78, 77, 45]
b. [78, 77, 45, 90, 98]
c. [98, 90, 45, 77, 78]
d. [78, 77, 45, 98, 90]
Answer: c. [98, 90, 45, 77, 78]
Q2) Given the following code, Which of the following is not a possible output?
import random
def calc():
x=random.random()
y=random.random()
z=random.random()
return( x+y+z )
print(calc())a. 1.6115945523495627
b. 0.202709723169674
c. 2.3142203461882844
d. 3.9133426822720435
Answer: d. 3.9133426822720435
Q3) Which of the following statement creates the given dictionary, d:{1:1,2:4,3:9,4:16}
a. d={x:x∗∗2 for x in range(1,5)}
b. d={x:x∗∗2 for x in range(5)}
c. d={x:x∗∗2 for x in range(0,5)}
d. d={x:x∗∗2 for x in range(1,6)}
Answer: a. d={x:x∗∗2 for x in range(1,5)}
These are Nptel Social Networks Week 1 Assignment Answers
Q4) Identify the graph output for the following segment:
import networkx as nx
import matplotlib.pyplot as plt
G=nx.Graph()
G.add_nodes_from([1,2,3,4,5])
G.add_edges_from([(1,2),(2,3),(2,5),(1,6),(3,4)])
nx.draw(G,with_labels=True)
plt.show()Answer: a
These are Nptel Social Networks Week 1 Assignment Answers
Q5) Given the following code, what is the number of edges in the graph created?
G=nx.Graph()
G.add_nodes_from([i for i in range(10)])
def create_graph(n):
while(len(G.edges())< n):
u=random.choice(list(G.nodes()))
v=random.choice(list(G.nodes()))
if u!=v and G.has_edge(u,v)==0:
G.add_edge(u,v)a. 9
b. 10
c. n
d. n−1
Answer: c. n
Q6) Which of the statements is True for the graph created from the statement: nx.gnp_random_graph(10,0.5)?
a. Graph has 10 nodes with half of the nodes connected
b. Graph has 10 nodes with each edge to be put with probability 0.5
c. Connected graph with 10 nodes
d. Graph has 5 nodes with half of the nodes connected
Answer: b. Graph has 10 nodes with each edge to be put with probability 0.5
These are Nptel Social Networks Week 1 Assignment Answers
Q7) What is the maximum number of graphs that can be created from 10 nodes?
a. 2(10 2)
b. 2(2 10)
c. 210
d. 210 2
Answer: a. 2(10 2)
Q8) Given that we have n nodes in a network, what is the approximate number of steps to search a node?
a. nlogn
b. logn
c. nn
d. 2logn
Answer: b. logn
These are Nptel Social Networks Week 1 Assignment Answers
Q9) Identify the layout for the graph given in the following figure.
a. spectral
b. spring
c. circular
d. random
Answer: c. circular
Q10) Friend suggestion on Facebook is one of the applications of
a. Page ranking
b. Link prediction
c. Small work phenomenon
d. Cascading
Answer: b. Link prediction
These are Nptel Social Networks Week 1 Assignment Answers
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