Nptel Social Networks Week 2 Assignment Answer

ABOUT THE COURSE :
The world has become highly interconnected and hence more complex than ever before. We are surrounded by a multitude of networks in our daily life, for example, friendship networks, online social networks, world wide web, road networks etc. All these networks are today available online in the form of graphs which hold a whole lot of hidden information. They encompass surprising secrets which have been time and again revealed with the help of tools like graph theory, sociology, game theory etc. The study of these graphs and revelation of their properties with these tools have been termed as Social Network Analysis.

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Nptel Social Networks Week 2 Assignment Answer

Course layout

Week 1: Introduction

Week 2: Handling Real-world Network Datasets

Week 3: Strength of Weak Ties

Week 4: Strong and Weak Relationships (Continued) & Homophily

Week 5: Homophily Continued and +Ve / -Ve Relationships

Week 6: Link Analysis

Week 7: Cascading Behaviour in Networks

Week 8: Link Analysis (Continued)

Week 9: Power Laws and Rich-Get-Richer Phenomena

Week 10: Power law (contd..) and Epidemics

Week 11: Small World Phenomenon

Week 12: Pseudocore (How to go viral on web)

Nptel Social Networks Week 2 Assignment Answer

Week 2 : Assignment 2

Due date: 2025-02-05, 23:59 IST.

Assignment not submitted

1 point

A graph has a diameter of 1. Which of the following statements must be true?

The graph is a complete graph.

All nodes in the graph are directly connected to every other node.

The graph contains the maximum possible number of edges for its number of nodes.

The graph is sparse with relatively fewer edges compared to nodes.

Adding or removing an edge cannot change its diameter.

1 point

In the Web Graph model, what do the nodes and edges represent?

Nodes are web pages, and edges are hyperlinks between them.

Nodes are servers, and edges are data transfer rates.

Nodes are users, and edges are user interactions.

Nodes are hashtags, and edges are co-occurrence frequencies.

1 point

A dataset represents a multigraph (a graph where multiple edges are allowed between two nodes). Which method in NetworkX allows you to load such a graph from an edge list file?

read_multiedgelist()

read_edgelist() with create_using=nx.MultiGraph()

read_gml()

read_multigraph()

1 point

Consider the following GML representation of a directed graph:

Which of the following correctly interprets the structure and properties of the graph described by the GML code?

The graph is undirected, with two nodes labeled “A” and “B” connected by two edges with weights 5 and 3, respectively.

The graph is directed, with node “A” pointing to node “B” with a weight of 5, and node “B” pointing back to node “A” with a weight of 3.

The graph is directed, with node “A” pointing to node “B” with a weight of 3, and node “B” pointing to node “A” with a weight of 5.

The graph is directed and contains a self-loop at node “A” with a weight of 5, and another self-loop at node “B” with a weight of 3.

1 point

Consider the following characteristics of different social network dataset formats. Which of the following statements is true about these formats?

Adjacency Matrix is most efficient for storing sparse graphs because it requires minimal space for large graphs with few edges.

Edge List format is not ideal for storing sparse graphs as it does not require extra space to store non-existing edges between nodes.

Adjacency List is space-efficient for sparse graphs and allows for fast traversal of neighbors, making it suitable for networks with relatively few edges compared to nodes.

Gephi File format is designed for large-scale networks and is not ideal for visualizing or analyzing graphs with edge weights or node attributes.

1 point

Pajek datasets are usually available in which of the following format?

.csv

.net

.txt

.tar

1 point

Which NetworkX function would you use to visualize the degree distribution of a graph?

nx.degree_histogram(G)

nx.closeness_centrality(G)

nx.shortest_path_length(G)

nx.eigenvector_centrality(G)

1 point

In Gephi, which metric would you compute to determine the connectivity between communities in a graph?

Modularity

Degree centrality

Closeness centrality

Betweenness centrality

1 point

In the context of graph theory, what is the critical threshold for the emergence of a giant connected component in a random graph?

When the number of edges equals the number of nodes.

When the average degree is 1.

When the clustering coefficient reaches 1.

When the network diameter becomes constant.

1 point

In a random graph G(n,p), when does a giant connected component typically emerge?

When the edge probability p is very small.

When the edge probability p is large enough to connect most nodes.

When the number of nodes n is very large.

When the graph has no isolated nodes.

Nptel Social Networks Week 2 Assignment Answer

Nptel Social Networks Week 2 Assignment Answer

Course layout

Nptel Social Networks Week 2 Assignment Answer

Week 2 : Assignment 2

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