NOC:Social Networks

Lecture 1 - Introduction

Lecture 2 - Answer to the puzzle

Lecture 3 - Introduction to Python - 1

Lecture 4 - Introduction to Python - 2

Lecture 5 - Introduction to Networkx - 1

Lecture 6 - Introduction to Networkx - 2

Lecture 7 - Social Networks: The Challenge

Lecture 8 - Google Page Rank

Lecture 9 - Searching in a Network

Lecture 10 - Link Prediction

Lecture 11 - The Contagions

Lecture 12 - Importance of Acquaintances

Lecture 13 - Marketing on Social Networks

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

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 - Embededness

Lecture 33 - Structural Holes

Lecture 34 - Social Capital

Lecture 35 - Finding Communities in a graph (Brute Force Method)

Lecture 36 - Community Detection Using Girvan Newman Algorithm

Lecture 37 - Visualising Communities using Gephi

Lecture 38 - Tie Strength, Social Media and Passive Engagement

Lecture 39 - Betweenness Measures and Graph Partitioning

Lecture 40 - Strong and Weak Relationship - Summary

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

Lecture 54 - Spatial Segregation : An Introduction

Lecture 55 - Spatial Segregation : Simulation of the Schelling Model

Lecture 56 - Spatial Segregation : Conclusion

Lecture 57 - Schelling Model Implementation - 1 (Introduction)

Lecture 58 - Schelling Model Implementation - 2 (Base Code)

Lecture 59 - Schelling Model Implementation - 3 (Visualization and Getting a list of boundary and internal nodes)

Lecture 60 - Schelling Model Implementation - 4 (Getting a list of unsatisfied nodes)

Lecture 61 - Schelling Model Implementation - 5 (Shifting the unsatisfied nodes and visualizing the final graph)

Lecture 62 - Chapter - 5 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

Lecture 75 - The Web Graph

Lecture 76 - Collecting the Web Graph

Lecture 77 - Equal Coin Distribution

Lecture 78 - Random Coin Dropping

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

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

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

Lecture 115 - Introduction to Powerlaw

Lecture 116 - Why do Normal Distributions Appear?

Lecture 117 - Power Law emerges in WWW graphs

Lecture 118 - Detecting the Presence of Powerlaw

Lecture 119 - Rich Get Richer Phenomenon

Lecture 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

Lecture 124 - Implementing a Random Graph (Erdos-Renyi Model) - 2

Lecture 125 - Forced Versus Random Removal of Nodes (Attack Survivability)

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 (Continued...)

Lecture 130 - Simple Branching Process for Modeling Epidemics

Lecture 131 - Simple Branching Process for Modeling Epidemics (Continued...)

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

Lecture 143 - Small World Effect - An 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

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