social networks nptel assignment answers 2022

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NPTEL Course "Social Networks" by Dr. S. R. S. Iyengar, IIT Ropar - Notes, Data sets and Programs

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Nptel-socialnetworks.

This repository contains the social networks course notes, network data sets and python programs for network analysis. Some of the surprising observations and beautiful discoveries achieved with Social Network Analysis are listed below.

• 6 degrees of separation: You can reach out to any person on this earth within an average of 6 hops. That means, "You know someone who knows someone who knows someone who knows someone who knows someone who knows Justin Beiber (or Angelina Jolie or literally anyone on this planet.)".
• The algorithm behind Google search: How does Google achieve such precise and valid search results? The underlying algorithm is fairly simple and relies totally on the network of web pages.
• How do you get your dream job: Not through your best friends but through your acquaintances to whom you talk relatively less frequently! Sounds counterintuitive.
• Link prediction: Can one predict who is going to be your next Facebook friend, or which product are you going to buy next on Flipkart, or which is the next movie you are going to watch on Netflix? Yes, it is possible.
• Viral Marketing: Want to make your new product sell out quickly? How do you determine the people to whom you should be giving the free samples? Does that even matter?
• Contagion: Not only information but happiness, obesity, altruism, depression all spread from person to person.

PRE-REQUISITES:

The course doesn’t assume any pre-requisites. We expect one has undergone a first course in basic programming.

COURSE INSTRUCTOR

Sudarshan Iyengar has a PhD from the Indian Institute of Science and is currently working as an assistant professor at IIT Ropar and has been teaching this course from the past 4 years.

COURSE LAYOUT

Week 1: Introduction to Graph Theory and Python

Week 2: Analyzing Online Social Network Datasets

Week 3: Power Law and Emergent Properties

Week 4: Strength of Weak Ties

Week 5: Homophily and Social Influence

Week 6: Structural Balance

Week 7: The Structure of the Web

Week 8: Link Analysis and Web Search

Week 9: Link Prediction

Week 10: Information Cascades

Week 11: Diffusion Behavior in Networks

Week 12: The Small World Phenomenon

Certification:

The criteria for certification in this course is different because of the online programming exam component. Please read the following carefully: Final score = Quiz score + Programming Assignment Score + Programming exam score + Proctored exam score Quiz score: 15% weightage with best 8 out of 12 Programming Assignment score: 10% weightage with best 4 out of 6 Online programming exam (unproctored): 10% weightage. Proctored exam (to be attended in person): 65% weightage

To pass the course and get a certificate: Final score >= 40/100 To get an Elite category of certificate: Final score >= 60/100 To get a gold medal stamp in the certificate: Final score >= 90/100

References:

https://onlinecourses.nptel.ac.in/noc18_cs02/preview

Contributors 2

• Jupyter Notebook 99.4%

Social Networks | NPTEL | Week 8 Assignment Solutions

This set of MCQ(multiple choice questions) focuses on the  Social Networks NPTEL Week 8 Assignment Solutions .

Course layout (Answers Link)

Answers COMING SOON! Kindly Wait!

Week 0: Assignment answers 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)

NOTE:  You can check your answer immediately by clicking show answer button. This set of “ Social Networks NPTEL 2022 Week 8 Assignment Solution” contains 10 questions.

Now, start attempting the quiz.

Social Networks NPTEL 2022 Week 8 Assignment Solutions

Q1. Let C be the unit circle with (0,0) as its origin in the XY – plane. Then A, the point at which the vector (6,8) intersects C, is

a) (0,0) b) (6, 8) c) (0.6, 0.8) d) (0.006, 0.008)

Answer: c) (0.6, 0.8)

Q2. Observe the graph shown in Figure 1, where A, B, P1, P2 and P3 are the points contained by the respective nodes. According to the principle of repeated improvement, which of the following is correct?:

a) A = P1 × P2,B = P1 × P3, P1 = A + B, P2 = A, P3 = B b) A = P1 + P2,B = P1 + P3, P1 = A × B, P2 = A, P3 = B c) A = P1 + P2,B = P1 + P3, P1 = A + B, P2 = A, P3 = B d) A = P1 + P2,B = P1 + P3, P1 = A × B, P2 = 0, P3 = 0

Answer: c) A = P1 + P2,B = P1 + P3, P1 = A + B, P2 = A, P3 = B

Q3. Given a vector (3, 4) in the XY plane, what will this vector become after being pulled to the unit circle as shown in the Figure 2?

a) 4/5, 3/5 b) 4/25, 3/25 c) 3/5, 4/5 d) 3/25, 4/25

Answer: c) 3/5, 4/5

Q4. For what values of pageranks of the nodes in Figure 3 does the process converge, i.e. pageranks of the nodes do not change after this configuration?

a) Node 1: 1/5, Node 2: 1/5, Node 3: 1/5, Node 4 = 1/5, Node 5 : 1/5 b) Node 1 : 1/5, Node 2 : 1/5, Node 3 : 2/5, Node 4 : 1/10, Node 5 = 1/10 c) Node 1 : 3/10, Node 2 : 1/10, Node 3 : 1/10, Node 4 : 2/10, Node 5 : 3/10 d) Node 1 : 1/10, Node 2 : 3/10, Node 3 : 2/10, Node 4 : 1/10, Node 5 = 3/10

Q5. In Hubs and Authorities algorithm, the authority update rule is defined as

a) For each page p, update auth(p) to be the sum of the hub scores of all pages that point to it. b) For each page p, update auth(p) to be the sum of the authority scores of all pages that it points to. c) For each page p, update auth(p) to be the sum of the hub scores of all pages that it points to. d) For each page p, update auth(p) to be the sum of the authority scores of all pages that points to it.

Social Networks NPTEL Week 8 Assignment Solutions

Q6. In the graph shown in the figure below, assume that the current page rank values of the nodes 0, 1, 2 and 3 are 0.2, 0.3, 0.4 and 0.5 respectively. What will be their pagerank values after one iteration?

a) 0.5 , 0.2, 0.3, 0.4 b) 0.6 , 0.3, 0.4, 0.5 c) 0.3, 0.4, 0.5. 0.6 d) 0.5 , 0.2, 0.4, 0.3

Q7. Given two linearly independent vectors v1 and v2, which of the following is true?

a) Any other vector can be written as the linear combination of v1 and v2. i.e. z=αv1+βv2. b) Any other vector can be written as sum of v1 and vv2. i.e. z=v1+v2. c) Any other vector can be written as difference of v1 and v2. i.e. z=v1−v2. d) Any other vector can be written as multiplication of v1 and v2. i.e. z=v1×v2

Q8. Given the graph as shown in the figure below, while calculating the pagerank using matrix multiplication method on this graph, how does the first matrix operation look like?

Social Networks NPTEL week 8 Assignment Solutions

Q9. When we add two vectors in the XY plane, where one vector has a very high magnitude as compared to the other, then the resultant vector is closer towards (in terms of direction) to

a) the bigger vector b) the smaller vector c) origin d) None of the above

Q10. In social networks

a) Hubs represent pointers and authorities represent resources b) Hubs represent resources and authorities represent pointers c) Both hubs and pointers represent resources d) Both hubs and pointers represent pointers

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• 1st Central Law Reviews: Expert Legal Analysis & Insights

• Computer Science and Engineering
• NOC:Social Networks (Video) 
• Co-ordinated by : IIT Ropar
• Available from : 2017-07-03
• Intro Video
• Introduction
• Answer to the puzzle
• Introduction to Python-1
• Introduction to Python-2
• Introduction to Networkx-1
• Introduction to Networkx-2
• Social Networks: The Challenge
• Google Page Rank
• Searching in a Network
• Link Prediction
• The Contagions
• Importance of Acquaintances
• Marketing on Social Networks
• Introduction to Datasets
• Ingredients Network
• Synonymy Network
• Social Network Datasets
• Datasets: Different Formats
• Datasets : How to Download?
• Datasets: Analysing Using Networkx
• Datasets: Analysing Using Gephi
• Introduction : Emergence of Connectedness
• Advanced Material : Emergence of Connectedness
• Programming Illustration : Emergence of Connectedness
• Summary to Datasets
• Granovetter's Strength of weak ties
• Triads, clustering coefficient and neighborhood overlap
• Structure of weak ties, bridges, and local bridges
• Validation of Granovetter's experiment using cell phone data
• Embededness
• Structural Holes
• Social Capital
• Finding Communities in a graph (Brute Force Method)
• Community Detection Using Girvan Newman Algorithm
• Visualising Communities using Gephi
• Tie Strength, Social Media and Passive Engagement
• Betweenness Measures and Graph Partitioning
• Strong and Weak Relationship - Summary
• Introduction to Homophily - Should you watch your company ?
• Selection and Social Influence
• Interplay between Selection and Social Influence
• Homophily - Definition and measurement
• Foci Closure and Membership Closure
• Introduction to Fatman Evolutionary model
• Fatman Evolutionary Model- The Base Code (Adding people)
• Fatman Evolutionary Model- The Base Code (Adding Social Foci)
• Fatman Evolutionary Model- Implementing Homophily
• Quantifying the Effect of Triadic Closure
• Fatman Evolutionary Model- Implementing Closures
• Fatman Evolutionary Model- Implementing Social Influence
• Fatman Evolutionary Model- Storing and analyzing longitudnal data
• Spatial Segregation: An Introduction
• Spatial Segregation: Simulation of the Schelling Model
• Spatial Segregation: Conclusion
• Schelling Model Implementation-1(Introduction)
• Schelling Model Implementation-2 (Base Code)
• Schelling Model Implementation-3 (Visualization and Getting a list of boundary and internal nodes)
• Schelling Model Implementation-4 (Getting a list of unsatisfied nodes)
• Schelling Model Implementation-5 (Shifting the unsatisfied nodes and visualizing the final graph)
• CHAPTER - 5 POSITIVE AND NEGATIVE RELATIONSHIPS (INTRODUCTION)
• STRUCTURAL BALANCE
• ENEMY'S ENEMY IS A FRIEND
• Characterizing the structure of balanced networks
• BALANCE THEOREM
• PROOF OF BALANCE THEOREM
• Introduction to positive and negative edges
• Outline of implemantation
• Creating graph, displaying it and counting unstable triangles
• Moving a network from an unstable to stable state
• Forming two coalitions
• Forming two coalitions contd
• Visualizing coalitions and the evolution
• The Web Graph
• Collecting the Web Graph
• Equal Coin Distribution
• Random Coin Dropping
• Google Page Ranking Using Web Graph
• Implementing PageRank Using Points Distribution Method-1
• Implementing PageRank Using Points Distribution Method-2
• Implementing PageRank Using Points Distribution Method-3
• Implementing PageRank Using Points Distribution Method-4
• Implementing PageRank Using Random Walk Method -1
• Implementing PageRank Using Random Walk Method -2
• DegreeRank versus PageRank
• Why do we Follow?
• Diffusion in Networks
• Modeling Diffusion
• Modeling Diffusion (continued)
• Impact of Commmunities on Diffusion
• Cascade and Clusters
• Knowledge, Thresholds and the Collective Action
• An Introduction to the Programming Screencast (Coding 4 major ideas)
• The Base Code
• Coding the First Big Idea - Increasing the Payoff
• Coding the Second Big Idea - Key People
• Coding the Third Big Idea- Impact of Communities on Cascades
• Coding the Fourth Big Idea - Cascades and Clusters
• Introduction to Hubs and Authorities (A Story)
• Principle of Repeated Improvement (A story)
• Principle of Repeated Improvement (An example)
• Hubs and Authorities
• PageRank Revisited - An example
• PageRank Revisited - Convergence in the Example
• PageRank Revisited - Conservation and Convergence
• PageRank, conservation and convergence - Another example
• Matrix Multiplication (Pre-requisite 1)
• Convergence in Repeated Matrix Multiplication (Pre-requisite 1)
• Addition of Two Vectors (Pre-requisite 2)
• Convergence in Repeated Matrix Multiplication- The Details
• PageRank as a Matrix Operation
• PageRank Explained
• Introduction to Powerlaw
• Why do Normal Distributions Appear?
• Power Law emerges in WWW graphs
• Detecting the Presence of Powerlaw
• Rich Get Richer Phenomenon
• Summary So Far
• Implementing Rich-getting-richer Phenomenon (Barabasi-Albert Model)-1
• Implementing Rich-getting-richer Phenomenon (Barabasi-Albert Model)-2
• Implementing a Random Graph (Erdos- Renyi Model)-1
• Implementing a Random Graph (Erdos- Renyi Model)-2
• Forced Versus Random Removal of Nodes (Attack Survivability)
• Rich Get Richer - A Possible Reason
• Rich Get Richer - The Long Tail
• Epidemics- An Introduction
• Introduction to epidemics (contd..)
• Simple Branching Process for Modeling Epidemics
• Simple Branching Process for Modeling Epidemics (contd..)
• Basic Reproductive Number
• Modeling epidemics on complex networks
• SIR and SIS spreading models
• Comparison between SIR and SIS spreading models
• Basic Reproductive Number Revisited for Complex Networks
• Percolation model
• Analysis of basic reproductive number in branching model (The problem statement)
• Analyzing basic reproductive number 2
• Analyzing basic reproductive number 3
• Analyzing basic reproductive number 4
• Analyzing basic reproductive number 5
• Small World Effect - An Introduction
• Milgram's Experiment
• The Generative Model
• Decentralized Search - I
• Decentralized Search - II
• Decentralized Search - III
• Programming illustration- Small world networks : Introduction
• Making homophily based edges
• Adding weak ties
• Plotting change in diameter
• Programming illustration- Myopic Search : Introduction
• Myopic Search
• Myopic Search comparision to optimal search
• Time Taken by Myopic Search
• PseudoCores : Introduction
• How to be Viral
• Who are the right key nodes?
• finding the right key nodes (the core)
• Coding K-Shell Decomposition
• Coding cascading Model
• Coding the importance of core nodes in cascading
• Pseudo core
• Live Session
• Live Session 10-04-2021
• Live Session 08-04-2021
• Live Session 07-09-2019
• Live Session 09-11-2019
• Live Session 26-10-2019
• Watch on YouTube
• Assignments
• Download Videos
• Transcripts

Module Name	Download
noc20_cs32_assigment_1
noc20_cs32_assigment_10
noc20_cs32_assigment_11
noc20_cs32_assigment_12
noc20_cs32_assigment_13
noc20_cs32_assigment_2
noc20_cs32_assigment_3
noc20_cs32_assigment_4
noc20_cs32_assigment_5
noc20_cs32_assigment_6
noc20_cs32_assigment_7
noc20_cs32_assigment_8
noc20_cs32_assigment_9

Sl.No	Chapter Name	MP4 Download
1	Introduction
2	Answer to the puzzle
3	Introduction to Python-1
4	Introduction to Python-2
5	Introduction to Networkx-1
6	Introduction to Networkx-2
7	Social Networks: The Challenge
8	Google Page Rank
9	Searching in a Network
10	Link Prediction
11	The Contagions
12	Importance of Acquaintances
13	Marketing on Social Networks
14	Introduction to Datasets
15	Ingredients Network
16	Synonymy Network
17	Web Graph
18	Social Network Datasets
19	Datasets: Different Formats
20	Datasets : How to Download?
21	Datasets: Analysing Using Networkx
22	Datasets: Analysing Using Gephi
23	Introduction : Emergence of Connectedness
24	Advanced Material : Emergence of Connectedness
25	Programming Illustration : Emergence of Connectedness
26	Summary to Datasets
27	Introduction
28	Granovetter's Strength of weak ties
29	Triads, clustering coefficient and neighborhood overlap
30	Structure of weak ties, bridges, and local bridges
31	Validation of Granovetter's experiment using cell phone data
32	Embededness
33	Structural Holes
34	Social Capital
35	Finding Communities in a graph (Brute Force Method)
36	Community Detection Using Girvan Newman Algorithm
37	Visualising Communities using Gephi
38	Tie Strength, Social Media and Passive Engagement
39	Betweenness Measures and Graph Partitioning
40	Strong and Weak Relationship - Summary
41	Introduction to Homophily - Should you watch your company ?
42	Selection and Social Influence
43	Interplay between Selection and Social Influence
44	Homophily - Definition and measurement
45	Foci Closure and Membership Closure
46	Introduction to Fatman Evolutionary model
47	Fatman Evolutionary Model- The Base Code (Adding people)
48	Fatman Evolutionary Model- The Base Code (Adding Social Foci)
49	Fatman Evolutionary Model- Implementing Homophily
50	Quantifying the Effect of Triadic Closure
51	Fatman Evolutionary Model- Implementing Closures
52	Fatman Evolutionary Model- Implementing Social Influence
53	Fatman Evolutionary Model- Storing and analyzing longitudnal data
54	Spatial Segregation: An Introduction
55	Spatial Segregation: Simulation of the Schelling Model
56	Spatial Segregation: Conclusion
57	Schelling Model Implementation-1(Introduction)
58	Schelling Model Implementation-2 (Base Code)
59	Schelling Model Implementation-3 (Visualization and Getting a list of boundary and internal nodes)
60	Schelling Model Implementation-4 (Getting a list of unsatisfied nodes)
61	Schelling Model Implementation-5 (Shifting the unsatisfied nodes and visualizing the final graph)
62	CHAPTER - 5 POSITIVE AND NEGATIVE RELATIONSHIPS (INTRODUCTION)
63	STRUCTURAL BALANCE
64	ENEMY'S ENEMY IS A FRIEND
65	Characterizing the structure of balanced networks
66	BALANCE THEOREM
67	PROOF OF BALANCE THEOREM
68	Introduction to positive and negative edges
69	Outline of implemantation
70	Creating graph, displaying it and counting unstable triangles
71	Moving a network from an unstable to stable state
72	Forming two coalitions
73	Forming two coalitions contd
74	Visualizing coalitions and the evolution
75	The Web Graph
76	Collecting the Web Graph
77	Equal Coin Distribution
78	Random Coin Dropping
79	Google Page Ranking Using Web Graph
80	Implementing PageRank Using Points Distribution Method-1
81	Implementing PageRank Using Points Distribution Method-2
82	Implementing PageRank Using Points Distribution Method-3
83	Implementing PageRank Using Points Distribution Method-4
84	Implementing PageRank Using Random Walk Method -1
85	Implementing PageRank Using Random Walk Method -2
86	DegreeRank versus PageRank
87	We Follow
88	Why do we Follow?
89	Diffusion in Networks
90	Modeling Diffusion
91	Modeling Diffusion (continued)
92	Impact of Commmunities on Diffusion
93	Cascade and Clusters
94	Knowledge, Thresholds and the Collective Action
95	An Introduction to the Programming Screencast (Coding 4 major ideas)
96	The Base Code
97	Coding the First Big Idea - Increasing the Payoff
98	Coding the Second Big Idea - Key People
99	Coding the Third Big Idea- Impact of Communities on Cascades
100	Coding the Fourth Big Idea - Cascades and Clusters
101	Introduction to Hubs and Authorities (A Story)
102	Principle of Repeated Improvement (A story)
103	Principle of Repeated Improvement (An example)
104	Hubs and Authorities
105	PageRank Revisited - An example
106	PageRank Revisited - Convergence in the Example
107	PageRank Revisited - Conservation and Convergence
108	PageRank, conservation and convergence - Another example
109	Matrix Multiplication (Pre-requisite 1)
110	Convergence in Repeated Matrix Multiplication (Pre-requisite 1)
111	Addition of Two Vectors (Pre-requisite 2)
112	Convergence in Repeated Matrix Multiplication- The Details
113	PageRank as a Matrix Operation
114	PageRank Explained
115	Introduction to Powerlaw
116	Why do Normal Distributions Appear?
117	Power Law emerges in WWW graphs
118	Detecting the Presence of Powerlaw
119	Rich Get Richer Phenomenon
120	Summary So Far
121	Implementing Rich-getting-richer Phenomenon (Barabasi-Albert Model)-1
122	Implementing Rich-getting-richer Phenomenon (Barabasi-Albert Model)-2
123	Implementing a Random Graph (Erdos- Renyi Model)-1
124	Implementing a Random Graph (Erdos- Renyi Model)-2
125	Forced Versus Random Removal of Nodes (Attack Survivability)
126	Rich Get Richer - A Possible Reason
127	Rich Get Richer - The Long Tail
128	Epidemics- An Introduction
129	Introduction to epidemics (contd..)
130	Simple Branching Process for Modeling Epidemics
131	Simple Branching Process for Modeling Epidemics (contd..)
132	Basic Reproductive Number
133	Modeling epidemics on complex networks
134	SIR and SIS spreading models
135	Comparison between SIR and SIS spreading models
136	Basic Reproductive Number Revisited for Complex Networks
137	Percolation model
138	Analysis of basic reproductive number in branching model (The problem statement)
139	Analyzing basic reproductive number 2
140	Analyzing basic reproductive number 3
141	Analyzing basic reproductive number 4
142	Analyzing basic reproductive number 5
143	Small World Effect - An Introduction
144	Milgram's Experiment
145	The Reason
146	The Generative Model
147	Decentralized Search - I
148	Decentralized Search - II
149	Decentralized Search - III
150	Programming illustration- Small world networks : Introduction
151	Base code
152	Making homophily based edges
153	Adding weak ties
154	Plotting change in diameter
155	Programming illustration- Myopic Search : Introduction
156	Myopic Search
157	Myopic Search comparision to optimal search
158	Time Taken by Myopic Search
159	PseudoCores : Introduction
160	How to be Viral
161	Who are the right key nodes?
162	finding the right key nodes (the core)
163	Coding K-Shell Decomposition
164	Coding cascading Model
165	Coding the importance of core nodes in cascading
166	Pseudo core

Sl.No	Chapter Name	English
1	Introduction
2	Answer to the puzzle
3	Introduction to Python-1
4	Introduction to Python-2
5	Introduction to Networkx-1
6	Introduction to Networkx-2
7	Social Networks: The Challenge
8	Google Page Rank
9	Searching in a Network
10	Link Prediction
11	The Contagions
12	Importance of Acquaintances
13	Marketing on Social Networks
14	Introduction to Datasets
15	Ingredients Network
16	Synonymy Network
17	Web Graph
18	Social Network Datasets
19	Datasets: Different Formats
20	Datasets : How to Download?
21	Datasets: Analysing Using Networkx
22	Datasets: Analysing Using Gephi
23	Introduction : Emergence of Connectedness
24	Advanced Material : Emergence of Connectedness
25	Programming Illustration : Emergence of Connectedness
26	Summary to Datasets
27	Introduction
28	Granovetter's Strength of weak ties
29	Triads, clustering coefficient and neighborhood overlap
30	Structure of weak ties, bridges, and local bridges
31	Validation of Granovetter's experiment using cell phone data
32	Embededness
33	Structural Holes
34	Social Capital
35	Finding Communities in a graph (Brute Force Method)
36	Community Detection Using Girvan Newman Algorithm
37	Visualising Communities using Gephi
38	Tie Strength, Social Media and Passive Engagement
39	Betweenness Measures and Graph Partitioning
40	Strong and Weak Relationship - Summary
41	Introduction to Homophily - Should you watch your company ?
42	Selection and Social Influence
43	Interplay between Selection and Social Influence
44	Homophily - Definition and measurement
45	Foci Closure and Membership Closure
46	Introduction to Fatman Evolutionary model
47	Fatman Evolutionary Model- The Base Code (Adding people)
48	Fatman Evolutionary Model- The Base Code (Adding Social Foci)
49	Fatman Evolutionary Model- Implementing Homophily
50	Quantifying the Effect of Triadic Closure
51	Fatman Evolutionary Model- Implementing Closures
52	Fatman Evolutionary Model- Implementing Social Influence
53	Fatman Evolutionary Model- Storing and analyzing longitudnal data
54	Spatial Segregation: An Introduction
55	Spatial Segregation: Simulation of the Schelling Model
56	Spatial Segregation: Conclusion
57	Schelling Model Implementation-1(Introduction)
58	Schelling Model Implementation-2 (Base Code)
59	Schelling Model Implementation-3 (Visualization and Getting a list of boundary and internal nodes)
60	Schelling Model Implementation-4 (Getting a list of unsatisfied nodes)
61	Schelling Model Implementation-5 (Shifting the unsatisfied nodes and visualizing the final graph)
62	CHAPTER - 5 POSITIVE AND NEGATIVE RELATIONSHIPS (INTRODUCTION)
63	STRUCTURAL BALANCE
64	ENEMY'S ENEMY IS A FRIEND
65	Characterizing the structure of balanced networks
66	BALANCE THEOREM
67	PROOF OF BALANCE THEOREM
68	Introduction to positive and negative edges
69	Outline of implemantation
70	Creating graph, displaying it and counting unstable triangles
71	Moving a network from an unstable to stable state
72	Forming two coalitions
73	Forming two coalitions contd
74	Visualizing coalitions and the evolution
75	The Web Graph
76	Collecting the Web Graph
77	Equal Coin Distribution
78	Random Coin Dropping
79	Google Page Ranking Using Web Graph
80	Implementing PageRank Using Points Distribution Method-1
81	Implementing PageRank Using Points Distribution Method-2
82	Implementing PageRank Using Points Distribution Method-3
83	Implementing PageRank Using Points Distribution Method-4
84	Implementing PageRank Using Random Walk Method -1
85	Implementing PageRank Using Random Walk Method -2
86	DegreeRank versus PageRank
87	We Follow
88	Why do we Follow?
89	Diffusion in Networks
90	Modeling Diffusion
91	Modeling Diffusion (continued)
92	Impact of Commmunities on Diffusion
93	Cascade and Clusters
94	Knowledge, Thresholds and the Collective Action
95	An Introduction to the Programming Screencast (Coding 4 major ideas)
96	The Base Code
97	Coding the First Big Idea - Increasing the Payoff
98	Coding the Second Big Idea - Key People
99	Coding the Third Big Idea- Impact of Communities on Cascades
100	Coding the Fourth Big Idea - Cascades and Clusters
101	Introduction to Hubs and Authorities (A Story)
102	Principle of Repeated Improvement (A story)
103	Principle of Repeated Improvement (An example)
104	Hubs and Authorities
105	PageRank Revisited - An example
106	PageRank Revisited - Convergence in the Example
107	PageRank Revisited - Conservation and Convergence
108	PageRank, conservation and convergence - Another example
109	Matrix Multiplication (Pre-requisite 1)
110	Convergence in Repeated Matrix Multiplication (Pre-requisite 1)
111	Addition of Two Vectors (Pre-requisite 2)
112	Convergence in Repeated Matrix Multiplication- The Details
113	PageRank as a Matrix Operation
114	PageRank Explained
115	Introduction to Powerlaw
116	Why do Normal Distributions Appear?
117	Power Law emerges in WWW graphs
118	Detecting the Presence of Powerlaw
119	Rich Get Richer Phenomenon
120	Summary So Far
121	Implementing Rich-getting-richer Phenomenon (Barabasi-Albert Model)-1
122	Implementing Rich-getting-richer Phenomenon (Barabasi-Albert Model)-2
123	Implementing a Random Graph (Erdos- Renyi Model)-1
124	Implementing a Random Graph (Erdos- Renyi Model)-2
125	Forced Versus Random Removal of Nodes (Attack Survivability)
126	Rich Get Richer - A Possible Reason
127	Rich Get Richer - The Long Tail
128	Epidemics- An Introduction
129	Introduction to epidemics (contd..)
130	Simple Branching Process for Modeling Epidemics
131	Simple Branching Process for Modeling Epidemics (contd..)
132	Basic Reproductive Number
133	Modeling epidemics on complex networks
134	SIR and SIS spreading models
135	Comparison between SIR and SIS spreading models
136	Basic Reproductive Number Revisited for Complex Networks
137	Percolation model
138	Analysis of basic reproductive number in branching model (The problem statement)
139	Analyzing basic reproductive number 2
140	Analyzing basic reproductive number 3
141	Analyzing basic reproductive number 4
142	Analyzing basic reproductive number 5
143	Small World Effect - An Introduction
144	Milgram's Experiment
145	The Reason
146	The Generative Model
147	Decentralized Search - I
148	Decentralized Search - II
149	Decentralized Search - III
150	Programming illustration- Small world networks : Introduction
151	Base code
152	Making homophily based edges
153	Adding weak ties
154	Plotting change in diameter
155	Programming illustration- Myopic Search : Introduction
156	Myopic Search
157	Myopic Search comparision to optimal search
158	Time Taken by Myopic Search
159	PseudoCores : Introduction
160	How to be Viral
161	Who are the right key nodes?
162	finding the right key nodes (the core)
163	Coding K-Shell Decomposition
164	Coding cascading Model
165	Coding the importance of core nodes in cascading
166	Pseudo core

Sl.No	Language	Book link
1	English
2	Bengali	Not Available
3	Gujarati	Not Available
4	Hindi	Not Available
5	Kannada	Not Available
6	Malayalam	Not Available
7	Marathi	Not Available
8	Tamil	Not Available
9	Telugu	Not Available

NPTEL Social Networks Week 1 Assignment Answers 2022

NPTEL Social Networks Week 1 Assignment Answers 2022 – Hello students in this article we are going to share NPTEL Social Networks Week 1 Assignment 2022 answers. All the Answers are provided below to help the students as a reference, You must submit your assignment with your own knowledge.

NPTEL Social Networks Week 1 Assignment Answers 2022 [July-Dec]

1. If there exist a graph where nodes represents students and edges represents friendship, then for a rumour to be spread across entire class – a. Every student must know every other student. b. The graph needs to be connected. c. The graph need not be connected. d. Will spread in any case.

2. If x = random.randrange(5,10), which values can x take?

• a. Only I, II, IV
• b. Only I, II, III
• c. Only II, III
• d. Only I, II

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3. If x = random.randint(3,6), which values can x take? I) 5 II) 4.3 III) 3 IV) 6

• a. Only I, II
• b. Only I, III
• c. Only I, III, IV

4. What will be the output of the following code snippet?

a. It is an even number b. It is an odd number c. Element does not exist d. The code won’t run

5. What will be the output of the following code snippet?

6. Maximum number of edges that can be present in a graph with 10 nodes are – a. 100 b. 45 c. 50 d. 55

👇 For Week 02 Assignment Answers 👇

7. For a complete graph Z with 5 nodes if A=z.order()/z.size(), what will be the value of A? a. 1/4 b. 1/8 c. 1/2 d. 1/16

8. What will nx.dijktra_path(G,u,v) return? a. Returns shortest path from u to v in a weighted graph b. Returns shortest path length c. Returns all possible paths from u to v d. Returns no. of possible paths from u to v

9. What will nx.gnp_random_graph(20,0.5) return? a. Returns graph with 20 nodes with half of the nodes connected. b. Returns graph with 20 nodes with each edge to be put with probability 0.5 c. Returns a connected graph with 10 nodes. d. Returns a graph with 10 nodes with each edge to be put with probability 0.5

10. Maximum number of graphs possible from 50 nodes are –

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About Social Networks:-

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.

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)

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course. Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

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Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

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Course layout, books and references.

• Networks, Crowds and Markets by David Easley and Jon Kleinberg, Cambridge University Press, 2010  (available for free download).
• Social and Economic Networks by Matthew O. Jackson, Princeton University Press, 2010.

Instructor bio

Prof. Sudarshan Iyengar

Prof. Poonam Saini

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NPTEL Social Networks ASSIGNMENT 3 Answers 2022

• by QuizXp Team
• August 13, 2022 August 13, 2022

NPTEL Social Networks ASSIGNMENT 3 Answers :- Hello students in this article we are going to share NPTEL Social Networks assignment week 3 answers. All the Answers provided below to help the students as a reference, You must submit your assignment at your own knowledge.

Below you can find NPTEL Social Networks ASSIGNMENT 3 Answers

Course Status :	Completed
Course Type :	Elective
Duration :	12 weeks
Category :
Credit Points :	3
Undergraduate
Start Date :	26 Jul 2021
End Date :	15 Oct 2021
Enrollment Ends :	09 Aug 2021
Exam Date :	23 Oct 2021 IST

Assignment No.	Answers
Assignment 1
Assignment 2
Assignment 3
Assignment 6
Assignment 7
Assignment 8

NPTEL Social Networks ASSIGNMENT 3 Answers 2022:-

Q1. If A denotes the set of friends Avanish has and B denotes the set of friends Bhavesh has, which of the following best describes their friendship’s neighbourhood overlap?

Q2. Which of the following statements is/are true with respect to the edge X in the graph given Below?

Q3. In the given graph, nodes represent people and edges represent friendships. If 𝐸 𝑖𝑗 = 𝐹𝑟𝑖𝑒𝑛𝑑𝑠ℎ𝑖𝑝 𝐸𝑚𝑏𝑒𝑑𝑑𝑒𝑑𝑛𝑒𝑠𝑠 𝑏𝑒𝑡𝑤𝑒𝑒𝑛 𝑖 & 𝑗, then choose the correct option.

Q4. Which of the following statements are correct about edge E in the following graph?

Q5. Ram reaches space and becomes friends with an alien community. What role/property of social networks is at display here with respect to Ram?

Next Week Assignment Answers

Q6. The audience of the Batman vs Superman fight are very passionate and there are equally large number of supporters on both sides. What properties would you expect in the network formed by the audience?

Q7. While implementing the Girvan Newman algorithm on a certain graph G, you observe that edge 𝐸 1 gets removed after 𝐸 2 . What can you comment about them?

Q8. If community A is a better community than B, then which of the following holds True?

Q9. How many connected components do you expect to see in a graph once the Girvan Newman algorithm halts when used for finding two good communities within the graph?

Q10. Choose the correct option based on the given two statements.

Statement I – The nodes at the ends of a local bridge in a graph have no common nodes.

Statement II – Removing bridges from a graph leads to a disconnected graph

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