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