NPTEL Social Networks Week 6 Assignment Answers 2025
1. Which of the following best describes a web graph?
- A graph with nodes representing users and edges representing their social connections
- A directed graph where nodes represent web pages and edges represent hyperlinks
- A graph representing data transmission in a network
- An undirected graph with nodes representing servers and edges representing data flows
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2. What is the main challenge in analyzing web graphs at a large scale?
- Ensuring the graph remains connected
- Handling the scale-free and sparse nature of the graph
- Maintaining an equal degree distribution for all nodes
- Converting directed graphs into undirected graphs
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3. In the equal coin distribution problem, the goal is typically to:
- Minimize the number of coins redistributed
- Maximize the sum of coins in the system
- Ensure every individual has the same number of coins after redistribution
- Distribute coins randomly among all individuals
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4. What condition must hold true for equal coin distribution to be possible in a system of N people with C total coins?
- C must be a multiple of N
- C must be an even number
- The graph must be fully connected
- N must be a prime number
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5. In random coin dropping simulations, which of the following distributions is often observed in the final system state?
- Gaussian distribution
- Uniform distribution
- Power-law distribution
- Exponential distribution
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6. What does random coin dropping often illustrate in terms of network behavior?
- Centralized control of resources
- Emergence of inequality in distributions over time
- Uniform redistribution of resources
- Cyclic patterns in resource allocation
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7. In Google’s PageRank algorithm, a node’s rank is determined by:
- Its in-degree only
- Its out-degree only
- The ranks of its incoming neighbors and their out-degrees
- The ranks of its outgoing neighbors and their in-degrees
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8. In the point distribution method for PageRank, what happens when a node distributes its rank points to its neighbors?
- It retains half of its points and distributes the rest equally
- It loses all its points in the next iteration
- It distributes all its points equally to its neighbors
- It distributes points based on the degree of neighbors
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9. How does the random walk model in PageRank deal with “dangling nodes” (nodes with no outgoing edges)?
- Dangling nodes are removed from the graph
- A damping factor redistributes their rank randomly
- Their rank is equally distributed among all other nodes
- Their rank remains constant in the system
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10. Which of the following best describes the difference between degree rank and PageRank in a web graph?
- Degree rank considers only the number of edges, while PageRank considers the importance of neighbors
- Degree rank is computed iteratively, while PageRank is calculated directly
- PageRank uses only in-degrees, while degree rank uses both in-degrees and out-degrees
- Degree rank is always higher than PageRank for nodes with high connectivity
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