Social Networks Week 2 NPTEL Assignment Answers 2025

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✅ Subject: Social Networks
📅 Week: 2
🎯 Session: NPTEL 2025 July-October
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NPTEL Social Networks Week 2 Assignment Answers 2025

1. Which of the following could be used as an indicator of a central ingredient in the Ingredients Network?

• High degree centrality
• Low clustering coefficient
• High betweenness centrality
• Minimum spanning tree membership

Answer : See Answers 

2. If the network is unweighted and undirected, which metric would best indicate an ingredient that connects various ingredient clusters?

• Degree centrality
• Betweenness centrality
• PageRank
• Closeness centrality

Answer :

3. Which of the following actions might increase the clustering coefficient of a node representing an ingredient?

• Adding edges among its neighbors
• Using it in recipes that already share many other ingredients
• Decreasing its degree
• Connecting it to an isolated node

Answer :

4. What is the best way to identify substitute ingredients in the network?

• Using PageRank
• Using community detection to find clusters of similar-use ingredients
• Finding the node with maximum degree
• Running BFS from each node

Answer :

5. Ingredients with high clustering coefficient are likely to:

• Appear in tightly-knit groups of recipes
• Be used rarely
• Be part of cultural or cuisine-specific subgroups
• Form bridges between disparate recipes

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6. The network of ingredients is most similar in structure to which type of network?

• Tree
• Star
• Small-world network
• Linear chain

Answer :

The Synonymy Network
A team of linguists constructs a network where each node is a word, and edges connect words that are synonyms. They analyze how meanings evolve over time and find that certain hubs like “big”, “good”, or “fast” have dozens of connections, while niche terms like “gargantuan” or “stellar” sit on the periphery.

7. What graph property makes a word like “good” a hub?

• High degree
• High centrality
• Low clustering coefficient
• High eigenvector falloff

Answer : See Answers

8. What type of network model is most appropriate for modeling the synonymy network?

• Tree
• Undirected unweighted graph
• Directed weighted graph
• Bipartite graph

Answer :

9. Why might peripheral nodes in the synonym network be important?

• They add richness to language use
• They might connect rarely used synonym groups
• They have highest centrality
• They always form cycles

Answer :

10. What kind of analysis can help group together words with similar meanings?

• DFS traversal
• Community detection
• Shortest path algorithm
• Bipartite projection

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11. In the synonymy network, a node with low degree but high betweenness might indicate:

• A bridge between two different meaning clusters
• A context-specific synonym
• A very frequent word
• An isolated component

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12. What type of network metric can help identify formal vs informal word usage patterns?

• PageRank
• Clustering within sociolect subgraphs
• Tree depth
• Diameter of network

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The Web Graph
The structure of the web is modeled as a directed graph, where nodes are webpages and edges represent hyperlinks. Researchers analyzing the web graph discover that certain sites like Wikipedia and government portals have high PageRank, while some newer sites have low visibility.

13. PageRank is influenced by:

• Number of incoming links
• Quality (PageRank) of linking pages
• Number of outgoing links
• Number of total users

Answer :

14. A page with many incoming links from low-ranked pages is likely to:

• Have very high PageRank
• Never rank at all
• Have modest PageRank depending on damping factor
• Break the algorithm

Answer :

15. In the web graph, a dangling node is:

• A node with no outgoing links
• A node with no incoming links
• A broken hyperlink
• A self-looped node

Answer :

16. Which graph algorithm is most appropriate to identify top influencers in the web graph?

• Dijkstra’s algorithm
• PageRank
• BFS
• Kruskal’s algorithm

Answer : See Answers

17. Which of these changes could improve a site’s PageRank?

• Getting linked from authoritative websites
• Reducing number of outbound links on important pages
• Removing internal links
• Adding self-loops

Answer :

18. The Web Graph most resembles:

• Undirected graph
• Balanced binary tree
• Bipartite graph
• Directed scale-free network

Answer :

Social Network Datasets – Analysis using Graphs
You are working with a dataset of a social media platform. Each user is a node, and edges represent friend connections. Using Python’s NetworkX, you aim to understand central influencers, isolate cliques, and simulate potential viral content spread.

19. Which Python libraries are best suited for analyzing such social network graphs?

• NetworkX
• Pandas (for data preprocessing)
• TensorFlow
• Seaborn

Answer :

20. What metric would best identify a user who connects disparate groups?

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

Answer :

21. Community detection can help:

• Identify social circles
• Predict content spread boundaries
• Compute path lengths
• Rank friends

Answer :

22. A tightly-knit group of users with many mutual friends is likely to form:

• A PageRank cluster
• A path component
• A community
• A bipartite set

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23. Which of these plots would be helpful in visualizing the degree distribution?

• Histogram
• Log-log plot
• Heatmap
• Confusion matrix

Answer :

24. What does a power-law distribution in degree typically suggest?

• Uniform connectivity
• Presence of hubs
• Graph is bipartite
• Random structure

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Emergence of Connectedness
As a new mobile messaging app gains popularity, its user network grows. Initially, users are isolated, but as invitations spread and group chats are created, a giant connected component emerges. Researchers study when this happens and how rapidly such transitions occur.

25. The sudden emergence of a giant connected component is an example of:

• Phase transition in networks
• Percolation theory in action
• Clique formation
• Graph diameter reduction

Answer : See Answers

26. As new edges are added randomly, when is a giant component most likely to emerge?

• After adding 10% of edges
• Near the critical threshold in Erdős–Rényi model
• Immediately
• When all nodes have same degree

Answer :

27. Factors influencing the rate of connectedness emergence include:

• Average degree
• Number of nodes
• Time of day
• Color of nodes

Answer :

28. Which real-world phenomenon reflects the same principle?

• A virus mutating
• A trending hashtag
• A viral video suddenly reaching millions
• Blockchain mining

Answer :

29. Which of these methods can detect when a giant component has formed?

• Analyzing component sizes over time
• Checking network diameter
• BFS from random node
• Comparing node colors

Answer :

30. In graph theory, a component is:

• A central node
• A directed edge
• A maximal connected subgraph
• A subgraph with cycles only

Answer :

Online Learning Platforms – Collaboration and Influence
In an online learning platform, users interact by asking and answering questions, collaborating on assignments, and sharing resources. Each user is a node, and interactions such as replies, comments, or co-enrollments are edges. Analysts are interested in understanding influence dynamics, collaborative learning patterns, and knowledge diffusion.

31. Which of the following network properties would best identify influential contributors?

• High in-degree (number of incoming replies/comments)
• High eigenvector centrality
• Low clustering coefficient
• Membership in isolated components

Answer : See Answers

32. Which metric would help detect tightly-knit learning groups?

• Degree centrality
• Clustering coefficient
• PageRank
• Edge betweenness

Answer :

33. A user with high betweenness centrality is likely to:

• Connect different learning communities
• Facilitate knowledge transfer between groups
• Be isolated from core discussions
• Have the most followers

Answer :

34. Which algorithm is most suitable for identifying natural study groups in the network?

• BFS
• Dijkstra’s algorithm
• Community detection (e.g., Girvan–Newman or Louvain)
• Kruskal’s algorithm

Answer :

35. Factors contributing to strong collaborative ties may include:

• Frequent co-participation in threads
• Repeated peer review or feedback
• Random logins
• Degree of anonymity

Answer :

36. In such a learning network, a node with high closeness centrality can:

• Disseminate information quickly to the whole network
• Only influence its direct neighbors
• Be easily removed without effect
• Belong to disconnected components

Answer : See Answers

NPTEL Social Networks Week 2 Assignment Answers 2024

1. Which of the following statements is/are True?

Statement I – Web graph is a directed network.
Statement II – Facebook friendship network is an undirected network.

Options:
A. I only
B. II only
C. Both
D. None

Answer: C. Both
Explanation:

• Web graphs are directed because hyperlinks go from one webpage to another (not necessarily both ways).
• Facebook friendships are mutual, so they form an undirected network.

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2. What is the clustering coefficient of a node that has 6 neighbors and 3 connections between those neighbors?

Options:
A. 0.2
B. 0.5
C. 0.75
D. 0.9

Answer: A. 0.2
Explanation:
Clustering Coefficient = (2 × number of connections between neighbors) / (k × (k−1))
= (2 × 3) / (6 × 5) = 6/30 = 0.2

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3. Name the method used to read dataset in ‘txt’ format.

Options:
A. read_gml()
B. read_edgelist()
C. read_txt()
D. read_gexf()

Answer: B. read_edgelist()
Explanation:
In NetworkX, read_edgelist() is used to load edge lists usually stored in .txt files.

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4. Given a complete graph with 120 vertices, what is the diameter of the Graph?

Options:
A. 0
B. 1
C. 2
D. 3

Answer: B. 1
Explanation:
In a complete graph, every node is connected to every other node directly. So, the maximum shortest path between any two nodes is 1.

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5. Which statement accurately reflects the characteristics of node degrees according to Power law?

Options:
A. Every individual in a social network has an equal number of connections
B. Exhibit a uniform distribution of connections among all users
C. A small number of individuals have a substantially higher number of connections compared to the majority
D. Each node has an identical degree, promoting equality in connectivity

Answer: C.
Explanation:
Power-law distribution is observed in real-world networks where a few nodes (hubs) have many connections, while most nodes have few.

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6. Given is a graph G with |V| = n nodes and |E| edges. In which of the following cases, we can guarantee that G is connected?

Options:
A. |E| = n
B. |E| = n−1
C. |E| = n(n−1)/2
D. |E| = n log₂n

Answer: C. |E| = n(n−1)/2
Explanation:
This is the number of edges in a complete graph. A complete graph is always connected.

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7. Which of the following statements is True for GML format of networks?

Statement I: Labels and attributes can be added
Statement II: Weights can be added

Options:
A. I only
B. II only
C. Both
D. None

Answer: C. Both
Explanation:
GML format supports both labels/attributes and weights. It is designed for flexible data representation.

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8. What is the reason for a path between words like “love” and “hatred” in the synonymy network?

Options:
A. Faulty edges
B. Contradictory paths to find antonyms
C. The network algorithm identifies unrelated words as synonyms
D. Words can undergo semantic shifts, acquiring new meanings or evolving to represent opposite concepts

Answer: D.
Explanation:
Language evolves over time. Words can develop new meanings that are contextually opposite, creating semantic bridges between antonyms.

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9. Given a graph with 5 nodes and 8 edges, find the density of the graph.

Hint: Use two-digit precision

Formula:
Density = 2×|E| / (|V| × (|V| − 1)) = (2×8)/(5×4) = 16/20 = 0.80

Answer: 0.80
Explanation:
The density shows how many edges exist compared to the maximum possible. Here, the graph is 80% dense.

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10. For any vertex v in an undirected (without loop, multiple edges), the clustering coefficient of v ranges from:

Options:
A. -1 to +1
B. 0 to 1
C. −∞ to +∞
D. 0 to +∞

Answer: B. 0 to 1
Explanation:
Clustering coefficient is a normalized value representing the degree of interconnection among neighbors. It ranges from 0 (no clustering) to 1 (complete clustering).