Course106106169166 lectures
Social Networks
Prof. Sudarshan Iyengar · IIT Ropar
About
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.
Intended audience: Any Interested Learners
Lectures
Week 1
- Lecture 01 - Introduction (9 min)
- Lecture 02 - Answer to the puzzle (6 min)
- Lecture 03 - Introduction to Python-1 (21 min)
- Lecture 04 - Introduction to Python-2 (28 min)
- Lecture 05 - Introduction to Networkx-1 (10 min)
- Lecture 06 - Introduction to Networkx-2 (45 min)
- Lecture 07 - Social Networks: The Challenge (4 min)
- Lecture 08 - Google Page Rank (2 min)
- Lecture 09 - Searching in a Network (2 min)
- Lecture 10 - Link Prediction (2 min)
- Lecture 11 - The Contagions (2 min)
- Lecture 12 - Importance of Acquaintances (1 min)
- Lecture 13 - Marketing on Social Networks (2 min)
Week 2
- Lecture 14 - Introduction to Datasets
- Lecture 15 - Ingredients Network
- Lecture 16 - Synonymy Network
- Lecture 17 - Web Graph
- Lecture 18 - Social Network Datasets
- Lecture 19 - Datasets: Different Formats
- Lecture 20 - Datasets: How to Download?
- Lecture 21 - Datasets: Analysing Using Networkx
- Lecture 22 - Datasets: Analysing Using Gephi
- Lecture 23 - Introduction : Emergence of Connectedness
- Lecture 24 - Advanced Material : Emergence of Connectedness
- Lecture 25 -Programming Illustration : Emergence of Connectedness
- Lecture 26 - Summary to Datasets
Week 3
- Lecture 27 - Introduction
- Lecture 28 - Granovetter's Strength of weak ties
- Lecture 29 - Triads, clustering coefficient and neighborhood overlap
- Lecture 30 - Structure of weak ties, bridges, and local bridges
- Lecture 31 - Validation of Granovetter's experiment using cell phone data
- Lecture 32 - Emeddedness
- Lecture 33 - Structural Holes
- Lecture 34 - Social Capital
- Lecture 35 - Tie Strength, Social Media and Passive Engagement
- Lecture 36 - Betweenness Measures and Graph Partitioning
- Lecture 37 - Finding Communities in a graph (Brute Force Method) - 1
- Lecture 38 - Community Detection Using Girvan Newman Algorithm
- Lecture 39 - Visualising Communities using Gephi
- Lecture 40 - Strong and Weak Relationship - Summary
Week 4
- Lecture 41 - Introduction to Homophily - Should you watch your company ?
- Lecture 42 - Selection and Social Influence
- Lecture 43 - Interplay between Selection and Social Influence
- Lecture 44 - Homophily - Definition and measurement
- Lecture 45 - Foci Closure and Membership Closure
- Lecture 46 - Introduction to Fatman Evolutionary model
- Lecture 47 - Fatman Evolutionary Model- The Base Code (Adding people)
- Lecture 48 - Fatman Evolutionary Model- The Base Code (Adding Social Foci)
- Lecture 49 - Fatman Evolutionary Model- Implementing Homophily
- Lecture 50 - Quantifying the Effect of Triadic Closure
- Lecture 51 - Fatman Evolutionary Model- Implementing Closures
- Lecture 52 - Fatman Evolutionary Model- Implementing Social Influence
- Lecture 53 - Fatman Evolutionary Model- Storing and analyzing longitudnal data
Week 5
- Lecture-54: Spatial Segregation: An Introduction
- Lecture55- Spatial Segregation: Simulation of the Schelling Model
- Lecture56- Spatial Segregation: Conclusion
- Lecture 57- Schelling Model Implementation-1(Introduction)
- Lecture 58- Schelling Model Implementation-2 (Base Code)
- Lecture 59- Schelling Model Implementation- Visualization and Getting a list of boundary and internal nodes
- Lecture 60- Schelling Model Implementation - Getting a list of unsatisfied nodes
- Lecture 61- Schelling Model Implementation - Shifting the unsatisfied nodes and visualizing the final graph
- Lecture 62- Positive and Negative Relationships - Introduction
- Lecture 63- Structural Balance
- Lecture 64- Enemy's Enemy is a Friend
- Lecture 65- Characterizing the structure of balanced networks
- Lecture 66- Balance Theorem
- Lecture 67- Proof of Balance Theorem
- Lecture 68- Introduction to positive and negative edges
- Lecture 69- Outline of Implemantation
- Lecture 70- Creating graph, displaying it and counting unstable triangles
- Lecture 71- Moving a network from an unstable to stable state
- Lecture 72- Forming two coalitions
- Lecture 73- Forming two coalitions (Continued)
- Lecture 74- Visualizing coalitions and the evolution
Week 6
- Lecture 75: The Web Graph
- Lecture 76: Collecting the Web Graph
- Lecture 77: Equal Coin Distribution
- Lecture 78: Random Walk Coin Distribution
- Lecture 79: Google Page Ranking Using Web Graph
- Lecture 80: Implementing PageRank Using Points Distribution Method-1
- Lecture 81: Implementing PageRank Using Points Distribution Method-2
- Lecture 82: Implementing PageRank Using Points Distribution Method-3
- Lecture 83: Implementing PageRank Using Points Distribution Method-4
- Lecture 84: Implementing PageRank Using Random Walk Method -1
- Lecture 85: Implementing PageRank Using Random Walk Method -2
- Lecture 86: DegreeRank versus PageRank
Week 7
- Lecture 87 - We Follow
- Lecture 88 - Why do we Follow?
- Lecture 89 - Diffusion in Networks
- Lecture 90 - Modeling Diffusion
- Lecture 91- Modeling Diffusion (Continued)
- Lecture 92 - Impact of Commmunities on Diffusion
- Lecture 93 - Cascade and Clusters
- Lecture 94 - Knowledge, Thresholds and the Collective Action
- Lecture 95 - An Introduction to the Programming Screencast (Coding 4 major ideas)
- Lecture 96 - The Base Code
- Lecture 97 - Coding the First Big Idea - Increasing the Payoff
- Lecture 98 - Coding the Second Big Idea - Key People
- Lecture 99 - Coding the Third Big Idea- Impact of Communities on Cascades
- Lecture 100 - Coding the Fourth Big Idea - Cascades and Clusters
Week 8
- Lecture 101 : Introduction to Hubs and Authorities (A Story)
- Lecture 102: Principle of Repeated Improvement (A story)
- Lecture 103: Principle of Repeated Improvement (An example)
- Lecture 104 : Hubs and Authorities
- Lecture 105 : PageRank Revisited - An example
- Lecture 106: PageRank Revisited - Convergence in the Example
- Lecture 107 : PageRank Revisited - Conservation and Convergence
- Lecture 108: PageRank, conservation and convergence - Another example
- Lecture 109 : Matrix Multiplication (Pre-requisite 1)
- Lecture 110: Convergence in Repeated Matrix Multiplication (Pre-requisite 1)
- Lecture 111 : Addition of Two Vectors (Pre-requisite 2)
- Lecture 112 : Convergence in Repeated Matrix Multiplication- The Details
- Lecture 113 : PageRank as a Matrix Operation
- Lecture 114 : PageRank Explained
Week 9
- Lecture 115 - Introduction to Power Law
- Lecture 116 - Why do Normal Distributions Appear?
- Lecture 117 - Power Law emerges in WWW graphs
- Lecture 118 - Detecting the Presence of Power Law
- Lecture 119 - Rich Get Richer Phenomenon
- Leture 120 - Summary So Far
- Lecture 121 - Implementing Rich-getting-richer Phenomenon (Barabasi-Albert Model)-1
- Lecture 122 - Implementing Rich-getting-richer Phenomenon (Barabasi-Albert Model)-2
- Lecture 123 - Implementing a Random Graph (Erdos- Renyi Model)-1
- Leture 124 - Implementing a Random Graph (Erdos- Renyi Model)-2
- Lecture 125 - Forced Versus Random Removal of Nodes (Attack Survivability)
Week 10
- Lecture 126 - Rich Get Richer - A Possible Reason
- Lecture 127 - Rich Get Richer - The Long Tail
- Lecture 128 - Epidemics- An Introduction
- Lecture 129 - Introduction to epidemics (contd..)
- Lecture 130 - Simple Branching Process for Modeling Epidemics
- Lecture 131 - Simple Branching Process for Modeling Epidemics (contd..)
- Lecture 132- Basic reproductive number
- Lecture 133- Modeling epidemics on complex networks
- Lecture 134 - SIR and SIS spreading models
- Lecture 135 - Comparison between SIR and SIS spreading models
- Lecture 136 - Basic Reproductive Number Revisited for Complex Networks
- Lecture 137 - Percolation model
- Lecture 138 - Analysis of basic reproductive number in branching model (The problem statement)
- Lecture 139 - Analyzing basic reproductive number 2
- Lecture 140 - Analyzing basic reproductive number (3)
- Lecture 141 - Analyzing basic reproductive number (4)
- Lecture 142 - Analyzing basic reproductive number (5)
Week 11
- Lecture 143 - Introduction
- Lecture 144 : Milgram's Experiment
- Lecture 145 : The Reason
- Lecture 146: The Generative Model
- Lecture 147 : Decentralized Search - I
- Lecture 148 : Decentralized Search - II
- Lecture 149 : Decentralized Search - III
Week 12
- Lecture 150 : Programming illustration- Small world networks : Introduction
- Lecture 151 : Base code
- Lecture 152 : Making homophily based edges
- Lecture 153 : Adding weak ties
- Lecture 154 : Plotting change in diameter
- Lecture 155 : Programming illustration- Myopic Search : Introduction
- Lecture 156 : Myopic Search
- Lecture 157 : Myopic Search comparision to optimal search
- Lecture 158 : Time Taken by Myopic Search
- Lecture 159 : PseudoCores : Introduction
- Lecture 160 : How to be Viral
- Lecture 161 : Who are the right key nodes?
- Lecture 162 : finding the right key nodes (the core)
- Lecture 163 : Coding K-Shell Decomposition
- Lecture 164 : Coding cascading Model
- Lecture 165 : Coding the importance of core nodes in cascading
- Lecture 166 : Pseudo core
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Ropar
- Department
- Computer Science and Engineering
- Teacher
- Prof. Sudarshan Iyengar
- Level
- Undergraduate
- Language
- English
- Subjects
- Computer science, Engineering
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge. Coordinated by IIT Madras.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed
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