Lecture 107 of 166Week 8
Lecture 107 : PageRank Revisited - Conservation and Convergence
Prof. Sudarshan Iyengar · IIT Ropar
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All the lectures
- 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)
- 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 - 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
- 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
- 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
- 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
- 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 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)
- 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)
- 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
- 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