Social Networks | Week 8
Session: JULY-DEC 2023
Course Name: Social Networks
Course Link: Click Here
These are Nptel Social Networks Week 8 Assignment 8 Answers
Q1. Consider a network of recommenders and resources, where higher rating represents a good one. What are the factors that contribute to a good rating for a node P? I. Good nodes pointing to P II. P pointing to good nodes I only II only Both I and II None
Answer: Both I and II
Q2. Pick out all the properties of a markov matrix largest eigen value is 1 smallest eigen value is 1 sum of any coumn is 1 sum of any column is 0
Answer: A, C
Q3. Identify whether the statements are True. Statement I – When a matrix is applied on its eigenvectors, the direction of the eigenvector only changes Statement II – Eigenvectors are linearly independent of each other I only II only II only None
Answer: II only
Q4. Consider a directed network having nodes A,B,C and edges (B,A), (A,C), (C,B), (B,C). The current pagerank values of and 0.2, 0.4 and 0.4 respectively. What will be their pagerank after one iteration? Apply the basic page update rule ignoring evaporation/teleportation. A:0.2,B:0.2,C:0.4 A:0.4,B:0.2,C:0.2 A:0.4,B:0.2,C:0.4 A:0.2,B:0.4,C:0.4
Answer: A:0.2,B:0.4,C:0.4
Q5. According to Google Page rank, a web page is important if a lot of other pages refer to this page the page recommends a lot of other pages important pages refer to this page unimportant pages refer to this page
Answer: important pages refer to this page
Q6. What will be the resultant vector when we apply the matrix A on the vector (3, 5)?A=[5142] (13, 35) (35, 13) (16, 22) (22, 16)
Answer: (35, 13)
Q7. If a Markov matrix X whose eigenvectors and eigenvalues are υ1,υ2 and λ1,λ2 respectively is applied on a vector V repeatedly k times, which of the following is true assuming we that we normalise the resultant vector after each iteration and λ1 is the greater eigenvalue and k is very large? AkV=υ1 AkV=υ2 AkV=υ1+υ2 AkV=υ1−υ2
Answer: A^kV=υ1
Q8. Pick out the matrix that represents the given graph H to view page rank as a matrix multiplication process: ⎡⎣⎢⎢⎢⎢0 1 1 0 1 0 0 0 0 1/2 0 1/2 1/2 1/2 0 0⎤⎦⎥⎥⎥⎥ ⎡⎣⎢⎢⎢⎢0 0 1 0 1 0 0 0 0 1/2 1 1/2 1/2 1/2 0 0⎤⎦⎥⎥⎥⎥ ⎡⎣⎢⎢⎢⎢0 0 1 0 1 0 0 0 1 1/2 0 1/2 1/2 1/2 0 0⎤⎦⎥⎥⎥⎥ ⎡⎣⎢⎢⎢⎢0 0 1 0 1 0 0 0 0 1/2 0 1/2 1/2 1/2 0 0⎤⎦⎥⎥⎥⎥
Answer: d. ⎡⎣⎢⎢⎢⎢0 0 1 0 1 0 0 0 0 1/2 0 1/2 1/2 1/2 0 0⎤⎦⎥⎥⎥⎥
Q9. Which of the following are TRUE for the Hubs and Authorities algorithm? Statement I – The Authority update rule states that for each page p, update auth(p) is the sum of the authority scores of all pages that point to it. Statement II – The Hub update rule states that for each page p, update hub(p) is the sum of the hub scores of all pages that it points to. I only II only Both None
Answer: None
Q10. What is the score value of authority(a) and hub(h) respectively for node 4 in the following figure after applying 1-step hub-authority computation (i.e. when k is 1)?Assume initial hub and authority of each node as 1. a(1)=3, h(1)=3 a(1)=0, h(1)=3 a(1)=3, h(1)=0 a(1)=0, h(1)=0
Answer: a(1)=3, h(1)=0
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Session: JAN-APR 2023
Q1. Consider the following graph G. What is the formula used to apply the principle of repeated improvement before normalisation to obtain convergence?
a. A = P1 + P2 + P4, B = P1 + P3, C = P2 + P4, P1 = A + B, P2 = A + C, P3 = B, P4 = A + C b. A = P1 + P3 + P4, B = P1 + P3, C = P2 + P4, P1 = A + B, P2 = A + C, P3 = B, P4 = A + C c. A = P1 + P2 + P4, B = P1 + P3, C = P2 + P4, P1 = A + B, P2 = A + C, P3 = B, P4 = A + B d. A = P1 + P2 + P3, B = P1 + P3, C = P2 + P4, P1 = A + B, P2 = A + C, P3 = B, P4 = A + C
Answer: a. A = P1 + P2 + P4, B = P1 + P3, C = P2 + P4, P1 = A + B, P2 = A + C, P3 = B, P4 = A + C
Q2. For the given network H, what will happen after say 10 iterations of Page rank updates for the initial value of 1/3 for every node in the network
a. The value at A steadily increases b. The value at C steadily decreases c. The value at C steadily increases d. The value at B steadily increases
Answer: c. The value at C steadily increases
Q3. Consider a Graph H shown in figure 2. Which of the following is True for this graph? a. Doesnot converge b. Converges with all points in node C c. Converges zero points in node C d. Converges with maximum points in A and B
Answer: b. Converges with all points in node C
Q4. Pick out the matrix that represents the given graph J to view page rank as a matrix multiplication process:
a. ⎡⎣⎢0011/201/2010⎤⎦⎥ b. ⎡⎣⎢001101010⎤⎦⎥ c. ⎡⎣⎢01/211/201/2010⎤⎦⎥ d. ⎡⎣⎢01/21/21/201/2010⎤⎦⎥
Answer: d. ⎡⎣⎢01/21/21/201/2010⎤⎦⎥
Given a matrix M, M=[4352] and a vector V=[23] Apply M on V and normalise it to obtain the resultant vector, R=[ab] Note: Precision level of at least four decimal places to be considered for computation
Q5. Enter the value of a ______________.
Answer: 0.88,0.89
Q6. Enter the value of b ______________.
Answer: 0.455,0.465
Q7. Which of the following are TRUE for the Hubs and Authorities algorithm? Statement I – The Authority update rule states that for each page p, update auth(p) is the sum of the hub scores of all pages that point to it. Statement II – The Hub update rule states that for each page p, update hub(p) is the sum of the authority scores of all pages that it points to. a. I only b. II only c. Both d. None
Answer: c. Both
Assume the shown pageranks for the given nodes at some point of time.
Q8. Find the page rank score of web pages B in the next iteration:
Answer: 6.5
Q9. Find the page rank score of web pages D in the next iteration:
Answer: 5.5
Q10. What is the score value of authority(a) and hub(h) respectively for node 5 in the following figure after applying 1-step hub-authority computation (i.e. when k is 1)?Assume initial hub and authority of each node as 1.
a. a(1)=3, h(1)=3 b. a(1)=3, h(1)=0 c. a(1)=0, h(1)=3 d. a(1)=0, h(1)=0
Answer: c. a(1)=0, h(1)=3
Q11. Two vectors V1 and V2 are added in the XY plane. Given that V1 is of a very high magnitude when compared to V2, then the resultant vector is a. same as V1 b. independent of V2 c. closer to the direction of V1 d. closer to the direction of V2
Answer: c. closer to the direction of V1
Q12. Which of the following is TRUE for a Markov Matrix? Statement I – The sum of elements in every column is same. Statement II – Highest eigenvalue of a Markov matrix is 1. a. I only b. II only c. Both d. None
Answer: b. II only
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NPTEL Social Network Analysis Week 3 Assignment Answers 2024
1. A network is said to follow small-world property if:
- Average path length α (log(network size))-1
- Average path length α (exp(network size))-1
- Average path length α log(network size)
- Average path length α exp(network size)
2. A giant component appears in a random network when the average degree of the network is:
- greater than or equal to 0.5
- less than or equal to 0.5
- less than or equal to 1
- greater than or equal to 1
3. Erdos-Renyi Random Network
- High clustering coefficient
- Small world property
- Degree distribution following power law
4. Watts-Strogatz Model
5. Barabasi-Albert Model
6. The clustering coefficient of a node is not (multiple-choice):
- Independent of p
- Dependent on p
- Dependent on the node’s degree
- Independent of the node’s degree
7. The principle of preferential attachment can also be thought of as:
- Rich attracts rich
- Poor gets richer
- Rich gets richer
- Rich gets poor
8. What are the different regimes in the plot below that characterize a random network?
- I: Supercritical regime; II: Critical point; III: Subcritical regime; IV: Connected regime
- I: Connected regime; II: Critical point; III: Supercritical regime; IV: Subcritical regime
- I: Subcritical regime; II: Critical point; III: Connected regime; IV: Supercritical regime
- I: Subcritical regime; II: Critical point; III: Supercritical regime; IV: Connected regime
9. What characteristic of the Barabási-Albert (BA) model distinguishes it from other random graph models?
- Each new node connects to a fixed number of existing nodes based on the node degree.
- All nodes have the same number of links.
- New nodes are added at random without preference.
- Nodes can be removed and added back into the system.
10. Based on the following formula for the clustering coefficient of a Watts-Strogatz model, determine the minimum and maximum values of CCWS, given that each node is connected to k/2 forward and k/2 backward nodes.
CC WS = 3(k-2)/4(k-1)
- 2, Infinity
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NPTEL Social Network Analysis Week 1 Assignment Answers 2024
1. Social network analysis cannot be used in the healthcare domain (e.g., to map susceptible people from infected ones).
2. In an ego-centric network, the central node is called the “alter” and its neighbors are “egos”.
3. What is the time complexity to check if an edge exists between two vertices i and j using an adjacency matrix?
4. How can you represent an undirected graph using an adjacency matrix?
- Using diagonal matrix
- Using upper triangular matrix
- Using symmetric matrix
- Using lower triangular matrix
5. How many edges are there in an undirected graph with 19 nodes?
6. Which of the following best describes an edge in a hypergraph?
- Always connects exactly two vertices.
- Can connect any number of vertices, including possibly more than two.
- Connects vertices in a linear chain.
- Cannot connect vertices but can connect other edges.
7. Which of the following is not a type of network analysis?
- Microscopic
- Macroscopic
9. What is a characteristic property of many real-world networks in terms of node connectivity?
- Randomly connectioned
- Uniform degree distribution
- Fully connected
10. Which of the following is not a link-centric view of the network?
- Ego-network
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NPTEL Social Network Analysis Assignment Answers 2024 (July-October)
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In this course you will get answers of all 12 weeks assignments of Social Network Analysis . Now we have uploaded the answers of Week 3 .
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Week 1 assignment answers (free), week 1 answers, week 2 assignment answers, week 2 answers, week 3 assignment answers, week 3 answers, week 4 assignment answers, week 4 answers.
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Social Networks | NPTEL 2022 | Week 2 Assignment Solutions
This set of MCQ(multiple choice questions) focuses on the Social Networks NPTEL Week 2 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 2 Assignment Solution” contains 10 questions.
Now, start attempting the quiz.
Social Networks NPTEL 2022 Week 2 Assignment Solutions
Q1. For any vertex v in an undirected (without loop, multiple edges) , the clustering coefficient of v ranges from:
a) -1 to +1 b) 0 to 1 c) −∞ to +∞ d) 0 to +∞
Answer: b) 0 to 1
Q2. Which of the following is not a network data set format?
a) Comma Separated Values b) GEphi XML Format c) Pajek NET d) GraphML
Answer: b) GEphi XML Format
Q3. Diameter of the complete graph on n vertices is
a) n-1 b) n-2 c) 0 d) 1
Answer: d) 1
Q4. Assuming Synonymy network of English language is undirected, we can traverse from the word ’FRIEND’ to ’ENEMY’ necessarily because:
a) the underlying network is connected. b) there must be an edge between them. c) the words have same number of letters. d) the Synonymy network is complete.
Answer: a) the underlying network is connected.
Q5. Let the given four real-world networks be A. Friendship network B. Road network C. E-mail network D. Citation network
Choose the correct option.
a) Only (A) is undirected b) Only (C) and (D) are directed. c) Only (C) is directed. d) All of them are undirected.
Answer: b) Only (C) and (D) are directed.
Social Networks NPTEL Week 2 Assignment Solutions
Q6. For reading a network file where the data is in the following form, which function should be used? node1 node2 node2 node3 node2 node5…….. where node1, node2, node3, node5 etc are ids of the nodes and node1 node2 indicates that there is an undirected edge between node1 and node2 and so on
a) nx.read_nodelist() b) nx.read_edgelist() c) nx.read_adjmat() d) Both nx.read_edgelist() and nx.read_adjmat() can be used
Answer: b) nx.read_edgelist()
Q7. State yes or no. Degree distribution of most real-world networks follows power law, which means there are very few nodes with very less degrees and there are a lot of nodes with very high degrees.
a) Yes b) No
Answer: b) No
Q8. What is the density of the graph H?
a) 1/2 b) 1/3 c) 1 d) 0
Answer: b) 1/3
Social Networks NPTEL week 2 Assignment Solutions
Q9. Given that the clustering coefficient of a vertex v is 0.3, and the number of edges between the neighbors of v is 3. What is the degree of the vertex v? (Graph considered is undirected, without loops and multiple edges)
a) 3 b) 4 c) 5 d) Insufficient information
Answer: c) 5
Q10. If G is a connected graph with n vertices, what is the minimum number of edges the graph G should have?
a) n b) n-1 c) n+1 d) n/2
Answer: b) n-1
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This repository contains the social networks course notes, network data sets and python programs for network analysis. NPTEL (National Programme on Technology Enhanced Learning) Social Networks - Python
PunithKumarMR/Social-Networks-NPTEL
Folders and files.
| Name | Name | |||
|---|---|---|---|---|
| 13 Commits | ||||
Repository files navigation
Social-network, introduction.
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.
Some of the surprising observations and beautiful discoveries achieved with Social Network Analysis are listed below.
1. six 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.)".
2. 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.
3. 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.
4. 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.
5. 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?
6. Contagion:
Not only information but happiness, obesity, altruism, depression all spread from person to person.
- Jupyter Notebook 99.4%
Social Networks
| Course Status : | Completed | |
| Course Type : | Elective | |
| Duration : | 12 weeks | |
| Category : | ||
| Credit Points : | 3 | Undergraduate |
| Start Date : | 22 Jan 2024 | |
| End Date : | 12 Apr 2024 | |
| Enrollment Ends : | 05 Feb 2024 | |
| Exam Registration Ends : | 16 Feb 2024 | |
| Exam Date : | 28 Apr 2024 IST |
| 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 |
IMAGES
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Social Network Analysis | NPTEL | Week 1 Assignment 1 Solution | July 2022 SaiTechiez 3.19K subscribers Subscribed 20 2.8K views 1 year ago NPTEL - Social Networks
Learn how to solve the first assignment of Social Network Analysis, a popular NPTEL course, with this detailed video tutorial.
We provide you NPTEL Assignment Answers 2024 and solutions of all courses. Week 1,2,3, 4, 5, 6, 7 , 8, 9, 10 ,11, 1. By Swayam platform.
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.
Want Nptel Social Networks Week 5 Assignment 5 Answers & Solutions. We have all for u All weeks of Social Networks available here on progiez.
Social Network Analysis. Networks are a fundamental tool for modeling complex social, technological, and biological systems. Coupled with the emergence of online social networks and large-scale data availability in social sciences, this course focuses on the analysis of massive networks which provide many computational, algorithmic, and ...
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.
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.
Want Nptel Social Networks Week 8 Assignment 8 Answers & Solutions. We have all for u All weeks of Social Networks available here on progiez.
NPTEL Social Network Analysis Week 3 Assignment Answers 2024. 1. A network is said to follow small-world property if: Average path length α (log(network size))-1
Welcome to our detailed walkthrough of the "NPTEL Social Network Analysis Week 2 Assignment Solution for July 2024," presented by IIT Delhi. This video is ta...
NPTEL Social Network Analysis Week 1 Assignment Answers 2024 1. Social network analysis cannot be used in the healthcare domain (e.g., to map susceptible people from infected ones).
In this course you will get answers of all 12 weeks assignments of Social Network Analysis. Now we have uploaded the answers of Week 3.
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.
Welcome to the NPTEL Week 1 Assignment on Social Network Analysis. In this comprehensive tutorial, we dive into the fascinating world of analyzing social networks and understanding their ...
This repository contains the social networks course notes, network data sets and python programs for network analysis. NPTEL (National Programme on Technology Enhanced Learning) Social Networks - ...
NPTEL provides E-learning through online Web and Video courses various streams.
This video is for providing on Social Network AnalysisThis video is for Education PurposeThis Course is provided by NPTEL - Online courses This video is ...
Her research primarily focuses on Social Network Analysis and Complex Networks. The major research projects include "Modeling Information Diffusion" and "Understanding Virality of Internet Memes" in online social networks.
NPTEL provides E-learning through online Web and Video courses various streams.
#Social Networks#NPTEL Assignment SolutionThe world has become highly interconnected and hence more complex than ever before. We are surrounded by a multitud...