Social Network Analysis Coursera Quiz Answers 2022 | All Weeks Assessment Answers [💯Correct Answer]

Hello Peers, Today we are going to share all week’s assessment and quiz answers of the Social Network Analysis course launched by Coursera totally free of cost✅✅✅. This is a certification course for every interested student.

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Here, you will find Social Network Analysis Exam Answers in Bold Color which are given below.

These answers are updated recently and are 100% correct✅ answers of all week, assessment, and final exam answers of Social Network Analysis from Coursera Free Certification Course.

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About Social Network Analysis Course

This course is intended to make a science’ of something at the heart of society: social networks.

Course Apply Link – Social Network Analysis

Social Network Analysis Quiz Answers

Week 01: Social Network Analysis Coursera Quiz Answers

Module 1 Quiz

Q.1. In Social Network Analysis, we have seen that researchers work fundamentally with two kinds of database matrixes:

  • One for describing who people are and one for who they will become
  • One for node attributes and one for links between nodes
  • One for node characteristics and one for node dynamics
  • One for economic attributes and one for social characteristics

Q.2. In Social Network Analysis, nodes must be individual people:

  • False, they must be social units of more than one person
  • True, since the network describes the social aspect between them
  • False, nodes can also be organizations, countries and other units

Q.3. You are fan of the services of a new company. Their taste is really peculiar, and they occupy a small niche of consumers with a peculiar taste, but you like it. They struggle with finding the right customers and almost shut down again, but then, suddenly, they start an amazing marketing campaign, where they make incredible offers to you and other existing customers if you convince three friends to try out their services. You know what concept their campaign draws on:

  • Network transitivity
  • Homophily
  • Closeness Centrality
  • Eigenvector Centrality
  • Six degrees of separation

Q.4. In Social Network Analysis, networks with different kinds of nodes are called:

  • Multipolar networks
  • Multimode networks
  • Multidimensional networks
  • Multiplex networks

Q.5. Which of the following is true?

  • Networks with different types of links are called multimode networks
  • Networks with different types of nodes are called multipolar networks
  • Networks with different kind of ties are called multiplex networks
  • Network with different kinds of relationships are called modalpolar networks

Q.6. Which of the following is the natural kind of network link to model a social network in a company?

  • Who are friends
  • Who helps each other
  • Who trust each other
  • Who gives advice
  • None of them

Q.7. Modeling social networks, it is often not clear what should be represented as nodes, and what as links. How do we choose links and nodes to model reality?

  • Depends on the statistical tool we want to use
  • Depends on the question we want to answer
  • Depends on the application being demographic or economic in nature
  • Depends on the application being economic or social in nature

Q.8. How did Granovetter’s model the ‘strength of weak ties’ in his famous paper? 

  • How much people liked each other
  • How frequently people interacted
  • How many contact hours people had
  • How much people knew about each other

Q.9. An adjacency matrix records:

  • Attributes of nodes
  • Dynamic evolution of the network
  • Links between nodes
  • Attributes of triangles

Week 02: Social Network Analysis Coursera Quiz Answers

Module 2 Quiz

Q.1 Network analysis has grown out of different academic disciplines, which leads to sometimes confusing nomenclatures. Which of the following has the same meaning as links/lines in networks?

  • Edges
  • Actors
  • Vertices
  • Sites

Q.2. What is A in the following network?

ngcb41
  • A dyad
  • A pendant
  • A self-tie
  • An isolate

Q.3. Which of the following is an undirected network?

  • Twitter follower network
  • Global trade network
  • Facebook friend network
  • High school friendship network

Q.4 What is the relationship between links and degrees in a network?

  • Each in-degree can have four out-degrees
  • Each link can have two degrees
  • Each in-link can have two out-degrees
  • Each degree can have two links

Q.5. In the network below, what CANNOT be used to describe the walk 1-2-3-1? 

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  • A path
  • A cycle
  • A triad closure
  • A geodesic return

Q.6. People often talk about ‘degrees of separation’ in network analysis. The more technical and precise term for the kind of ‘separation’ they are talking about is: 

  • Walk distance
  • Betweenness centrality
  • Multiplex strength of ties
  • Geodesic distance
  • Cycle interval

Q.7. A colleague of your aunt’s friend knows Lionel Messi. How many degrees of separation are between you and the famous soccer player?

  • Four degrees of separation
  • Six degrees of separation
  • Two degrees of separation
  • Five degrees of separation

Q.8.In Social Network Analysis, there is a clear mathematical meaning if you are part of a ‘clique’. It means that:

  • Members maintain an exclusive group and are not welcoming to outsiders
  • Everybody of the group is connected to everyone else in this group
  • Members are held together by common interests, views, or purposes
  • Other groups are dominated by one (cliquish) group

Q.9. Which type of centrality is best for defining the center of the network?

  • Degree centrality
  • Betweenness centrality
  • Closeness centrality
  • There is no best type
  • Eigenvector centrality

Q.10. When you assess the number of connections of nodes in a network, you are effectively assessing:

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

Q.11. When you assess how distant nodes are from each other in a network, you are effectively assessing:

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

Q.12. Which statement is correct? Betweenness Centrality measures:

  • The number of connections between connected nodes
  • How many links are between indirect friends of friends
  • The number of links between connected nodes
  • How often nodes are on shortest paths

Q.13. Which statement is correct? Eigenvector Centrality considers:

  • The number of links of connected nodes
  • How long a cycle is where a node starts and ends with itself
  • The number of clusters in the network
  • The self-paths among shortest paths between nodes

Q.14. Your new employer aims to fight the spread of fake news with a new algorithm designed by a group of ingenious computational social scientists. The problem is that running the algorithm is so expensive that it can only be installed at one single point in a given network. The goal is to delete fake news as often as possible when it spreads from everyone to everyone in the network. You are the newest member of this all-star team, and it is your task to determine where at which node to install it. You suggest to detect the node with the optimal:

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

Q.15. Which node has the highest closeness centrality? What are the “raw closeness counts” for [A,B,C]?

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  • [9,7,8]
  • [9,9,8]
  • [8,10,7]
  • [9,8,7]

Week 03: Social Network Analysis Coursera Quiz Answers

Module 3 Quiz

Q.1 Which longstanding finding from the Communication studies can explain the reason marketing research focus is so much on influencers?

  • One-step flow
  • Two-step flow
  • Diffusion of innovation
  • Tipping point contagion

Q.2. It is a fact that the top five tweets are usually all posted by celebrities (influencers). Knowing this, which of the following statements is true?

  • Only famous people (influencers) can create truly viral campaigns.
  • Normal people like you and me can be accidental influencers and create viral campaigns

Q.3. What is one of the typical signatures researchers find when they study so-called Twitter cascades?

  • The possibility that tweets get ignored is equal to the ratio of the tweeting frequency, divided by the number of followers
  • Most of the tweets are tweeted thousands of times while a small fraction of tweets go nowhere
  • The possibility that tweets get retweeted is linearly correlated with the positive likes received by the person who tweets
  • The vast majority of the tweets are never reposted, while a small fraction of tweets gets retweeted thousands of times

Q.4. Which of the following is a case of base rate fallacy?

  • The probability of an influencer produces a cascade is not equal to the probability that an influencer produces a viral tweet
  • The probability that a tweet from an influencer goes viral is not equal to the probability that a viral tweet comes from an influencer
  • The probability of a person has a certain disease does not equal to the probability that a person has any symptom of that disease
  • The probability that a person is an influencer is not equal to the probability that a tweet is viral

Q.5. When predicting which online post go viral, Prof. Lamberson from UCLA told us that the most influential variable is:

  • The number of followers of the poster
  • If the message was sent by a celebrity
  • The structure of the underlying network
  • If the person has been influential in the post directly preceding this post

Week 04: Social Network Analysis Coursera Quiz Answers

Module 4 Quiz

Q.1. We have seen that in network science, it is assumed that networks are being produced by one of the following four different ways: random networks; scale-free networks; small world networks; hub&spoke / star network.

  • False
  • True

Q.2. Why do we study random networks?

  • No specific reason, this choice is just random
  • Because most social networks follow a random network structure
  • Because it helps us decide if a network is special
  • Because it is an abstraction of empirical research in social science
  • Because it provides a most simplistic model

Q.3. When simulating certain kinds of networks to derive certain network properties (such as the average degree, or the number of triangles), one can use numerical and analytical approaches. What is the difference?

  • For numerical solutions, you use traditional probabilistic calculus, for analytical solutions you use modern machine learning
  • For analytical solutions, you enumerate the options and basically count, for numerical solutions you use math to derive the results
  • For numerical solutions, you enumerate the options and basically count, for analytical solutions you use math to derive the results
  • For analytical solutions, you use induction, for numerical solutions you use deduction

Q.4. When studying phenomena like the spread of diseases, the diffusion of innovations, or the virality of opinions, the so-called giant component plays an important role. What have we seen regarding how giant components grow from random networks?

  • Small world networks are found at the sweet spot between giant components and network hubs
  • The majority of nodes are connected to the giant component rather suddenly after passing a certain tipping point or threshold
  • Giant components only emerge in scale-free networks, where there are exponentially few nodes many links
  • A giant component will only emerge after every node in the network has sufficient Eigenvector triality

Q.5. In network analysis, what does “preferential attachment” mean?

  • The Erdos-Renyi random graph exhibits a threshold function with a giant component
  • Fat tails are scale-free fractals, with exponentially few nodes with exponentially many connection
  • The probability of a node to connect with new nodes, corresponds to the number of existing degrees of a node

Q,6. What does it mean that something is distributed according to a power-law?

  • Exponentially few, have exponentially much, and exponentially many, have exponentially little
  • 68% of the cases lie within one standard deviation and 95% within two standard deviations
  • 80 percent of outcomes (outputs) come from 20 percent of causes (inputs), aka the 80-20 rule
  • A list of hubs of scale-free popular kids on the block becomes a straight line when taking a logarithm

Q.7. We got to know a selection of stylized mechanisms that grow network structures that are typically found in social networks. Which of the following is true about them?

  • Erdos-Renyi networks have a high level of clustering and small average path length
  • Scale-free networks can be grown by a logic of preferential attachment
  • Degree distributions in small world networks follow a power law distribution
  • Small world networks are also known as Erdos-Renyi networks

Q.8.When social scientists say that societies consists of small world networks, what do they mean?

  • Exponentially few, have exponentially much, and exponentially many, have exponentially little
  • People have both close connections in tight groups and quick access to everybody
  • People unexpectedly often meet people with whom they have common acquaintances (“what a small world?!”)
  • People can reach all other people in the network quickly (small closeness centrality)

Week 05: Social Network Analysis Coursera Quiz Answers

Module 5 Quiz

Q.1 Airline routes often evolve toward so-called hub-and-spoke or start-network configurations. Why is that?

  • Because they have low costs in maintaining links, but high benefits
  • Due to the their robustness, even if nodes fail
  • Because they have small average path length
  • Star networks have high clustering coefficients, allowing for groups to compete

Q.2 If there is almost no cost to create and maintain connections, but large benefits, what network will evolve?

  • A clique
  • Star network
  • Scale free network
  • Small world network

Q.3 In lecture, we grew efficient networks by adding links, until they were stable (following Jackson’s symmetric connection model). We assumed commonly used variables that eventually shaped and guided the evolution of the network. Which was NOT one of them?

  • Indirect benefits from connections not directly linked to the node
  • Benefits from maintaining direct connections
  • Cost of maintaining direct connections
  • Cost of maintaining indirect connections not directly linked to the node

Q,4 In the lecture, when talking about dynamic networks, how did we define “social efficiency”?

  • Redistribution mechanisms were used to introduce social fairness among people
  • No node in the network can improve its situation
  • It has the largest sum of the net benefits (benefit-costs) for the whole society
  • The social benefit is fairly distributed among everybody
  • The social network is stable and does not change during the time of observation

Q.5 In order to have a stable network configuration, a network must reach _______. This is when _______ changes in the network. Fill in the blanks with:

  • Evenness; everything
  • Symmetry; nothing
  • Equilibrium; everything
  • Equilibrium; nothing

Q.6 When talking about dynamic networks in lecture, how did we define “social stability”?

  • Redistribution mechanisms were used to introduce social fairness among people
  • No node in the network can improve its situation
  • The social network does not change during the time of observation
  • It has the largest sum of the net benefits (benefit-costs) for the whole society
  • The social network does not change for half of its lifetime

Q.7 In the lecture, after we grew a uniquely efficient network we found that it was unstable. What did we do to stabilize the efficient structure of our network?

  • We redistributed resources, balancing costs, and benefits
  • We added extra nodes, alleviating the burden on individual nodes
  • We reconfigured the network to a small world network structure
  • We added extra links that created more stable paths
  • We used taxing/bargaining to assure that most nodes were well off

Q.8 If a disease is spreading in a scale-free network, and you want to stop it with vaccination efficiently, what would you do?

  • Vaccinate the nodes with high clustering coefficient
  • Vaccinate the nodes with six degrees of separation
  • Vaccinate randomly, following Erdos-Renyi
  • Stabilize the network with cross-subsidies
  • Vaccinate the hubs of the network

Q.9. Why do you need less steps to diffuse something in a scale-free network, compared with a random network?

  • Central hubs can reach many nodes very quickly
  • Scale-free small world Erdos-Renyi graphs follow preferential attachment
  • Exponentially many nodes are hubs and catapult the messages to many others

More About This Course

This course is intended to make a science’ of something at the heart of society: social networks.

Humans are natural network scientists because we constantly, almost unconsciously, compute new network configurations when thinking about friends and family (which are specific types of social networks), colleagues and organizational relations (other, overlapping network structures), and how to navigate delicate or opportunistic network configurations to save guard or advance in our social standing (with society being one big social network itself).

While such network structures have always existed, computational social science has assisted in revealing and studying them more thoroughly. T

he first section of the course focuses on network structure. This appears as static snapshots of networks, which can be complex and reveal important aspects of social systems. You will also visualize and analyze a network with software in our hands-on lab, which will help you appreciate the complexity that social networks can take on.

The second part of the course will examine how networks evolve over time. We want to know how we can predict what kind of network will form and whether or not we can influence network dynamics.

WHAT WILL YOU LEARN?

  • Define networks and learn about the languages they use.
  • Analyze a social network by manipulating data and visualizing a network.
  • Discuss the mechanisms that cause networks to form.
  • Using case studies, investigate social network analysis.

Conclusion

Hopefully, this article will be useful for you to find all the Week, final assessment, and Peer Graded Assessment Answers of the Social Network Analysis Quiz of Coursera and grab some premium knowledge with less effort. If this article really helped you in any way then make sure to share it with your friends on social media and let them also know about this amazing training. You can also check out our other course Answers. So, be with us guys we will share a lot more free courses and their exam/quiz solutions also, and follow our Techno-RJ Blog for more updates.

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