Unit 7 - Week 3- Strength of Weak Ties

28/12/2017 Social Networks - - Unit 7 - Week 3- Strength of Weak Ties

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Unit Announcements Course Forum Progress Mentor 7 - Week 3- Strength of Weak Ties

Course outline Week3- Assignment 1 . Course Trailer Submitted assignment FAQ 1) In social networks, friends and acquaintances respectively lead to: 1 point

Things to Note Strong ties, weak ties Weak ties, strong ties Accessing the Portal Both lead to strong ties Both lead to weak ties Week 1 - Introduction

Week 2 - Accepted Answers: Handling Real- Strong ties, weak ties world Network Datasets 2) If four nodes form a complete graph, then what will be their clustering coefficient? 1 point

Week 3- Strength 1/4, 1/4, 1/4, 1/4 of Weak Ties 0, 0, 0, 0 1, 1, 1, 1

4, 4, 4, 4

Lecture 27 - Introduction Accepted Answers: 1, 1, 1, 1

3) Granovetter argued that while searching for a new job: 1 point

Friends are important. Acquaintances are important. None of the friends or acquaintances are important. Both friends and acquaintances are important. Lecture 28 - Granovetter's Strength of weak ties Accepted Answers: Acquaintances are important.

4) Triadic closure implies that: 1 point

Two people having a common enemy have more probability of becoming friends with each other. https://onlinecourses.nptel.ac.in/noc17_cs41/unit?unit=71&assessment=72 1/5 28/12/2017 Social Networks - - Unit 7 - Week 3- Strength of Weak Ties Three people having a common enemy have more probability of becoming friends with each other. Lecture 29 - Triads, Two people having a common friend have more probability of becoming friends with each other. clustering Two people having a common person as a distant acquaintance have more probability of coefficient and becoming friends with each other. neighborhood overlap

Accepted Answers: Two people having a common friend have more probability of becoming friends with each other.

5) Choose whatever is True out of the following: 1 point

Triadic closure leads to triangles.

Triangles lead to triadic closure. Lecture 30 - There is no connection between triadic closures and traingles. Structure of Triadic closures and triangles are synonymous. weak ties, bridges, and local bridges

Accepted Answers: Triadic closure leads to triangles.

6) As per the definition of span, what would be the span of a bridge in a social network? 1 point

1 Infinite 0 Lecture 31 - Validation of Depends on the network structure. Granovetter's

experiment using cell phone data Accepted Answers: Infinite

7) Girvan Newman Method is used for: 1 point

Computing Clustering Coefficient Finding Triadic Closure

Detecting Communities

Calculating Embeddedness Lecture 32 - Emeddedness

Accepted Answers:

Detecting Communities

8) What is rare in a real world social network? 1 point

Bridges Local Bridges

Lecture 33 - Triadic Closures Structural Holes Triangles

Accepted Answers: Bridges

9) Weak ties are important because: 1 point

They might later become strong ties. Lecture 34 - They provide connections across communities. Social Capital They connect nodes with difficult-to-reach parts of the network.

https://onlinecourses.nptel.ac.in/noc17_cs41/unit?unit=71&assessment=72 2/5 28/12/2017 Social Networks - - Unit 7 - Week 3- Strength of Weak Ties Both (B) and (C)

Accepted Answers: Both (B) and (C)

Lecture 35 - 10)Girvan Newman Method is based on the concept of: 1 point Finding Communities in Node Betweenness a graph (Brute Force Method) - Edge Betweenness 1 Node Clustering Coefficient Node Degree

Accepted Answers: Edge Betweenness

11)As per the well-known history of Karate club, in the end, the club got divided into how many 1 point

Lecture 36 - communities?: Community Detection Using 1 Girvan Newman 2 Algorithm 3 4

Accepted Answers: 2

12)Which of the following is True with respect to Girvan Newman Method: 1 point Lecture 37 - Visualising It starts from a set of nodes of the given graph with no edges, and keeps adding the edges one Communities by one based on some criteria. using Gephi It starts from the given graph with all the nodes and edges and keeps removing the edges based on some criteria. It removes the edges one by one and then computes the clustering coefficient of all the nodes.

It adds the edges one by one and then computes the clustering coefficient of all the nodes.

Accepted Answers:

It starts from the given graph with all the nodes and edges and keeps removing the edges based on some Quiz : Week3- criteria. Assignment 1 13)Computing betweenness Centrality of a given node involves computing which of the 1 point following?:

The number of shortest paths between the given node and the highest degree node. The number of longest paths between the given node and the highest degree node.

The number of shortest paths that pass through the given node.

The number of longest paths that pass through the given node.

Feedback for week 3 Accepted Answers: The number of shortest paths that pass through the given node.

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Solutions to Week3- Assignment1

Week4 - - 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?)

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