SOCIAL NETWORKING [ALL CONCEPTS], Study Guides, Projects, Research of Social structure and social organization

Download SOCIAL NETWORKING [ALL CONCEPTS] and more Study Guides, Projects, Research Social structure and social organization in PDF only on Docsity!

SOCIAL NETWORKS

Prof. Sudarshan Iyengar

Computer Science andEngineering

IIT Madras

INDEX

- Week S.NO TOPICS PAGE.NO

• 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 PageRank
• 9 Searching in a Network
• 10 Link Prediction
• 11 The Contagions
• 12 Importance of Acquaintances
• 13 Marketing on Social Networks - Week
• 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: Analyzing Using Networkx
• 22 Datasets: Analyzing Using Gephi
• 23 Introduction : Emergence of Connectedness
• 24 Advanced Material : Emergence of Connectedness
• 25 Programming Illustration : Emergence of Connectedness
• 26 Summary to Datasets
  • Week
• 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 Embeddedness
• 33 Structural Holes
• 34 Social Capital
• 35 Finding Communities in a graph (Brute Force Method)
• 36 Community Detection Using Girvan Newman Algorithm
• 37 Visualizing Communities using Gephi
• 38 Tie Strength, Social Media and Passive Engagement
• 39 Betweenness Measures and Graph Partitioning
• 40 Strong and Weak Relationship - Summary
  • Week
• 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 longitudinal data - Week
• 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)
  • boundary and internal nodes) Schelling Model Implementation-3 (Visualization and Getting a list of
  • nodes) Schelling Model Implementation-4 (Getting a list of unsatisfied
  • and visualizing the final graph) Schelling Model Implementation-5 (Shifting the unsatisfied nodes
  • (INTRODUCTION) CHAPTER - 5 POSITIVE AND NEGATIVE RELATIONSHIPS
• 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 implementation
• 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 - Week
• 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 - Week
• 87 We Follow
• 88 Why do we Follow?
• 89 Diffusion in Networks
• 90 Modeling Diffusion
• 91 Modeling Diffusion (continued)
• 92 Impact of Communities on Diffusion
• 93 Cascade and Clusters
• 94 Knowledge, Thresholds and the Collective Action
  • ideas) An Introduction to the Programming Screencast (Coding 4 major
• 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 - Week
• 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 (Prerequisite 1)
• 110 Convergence in Repeated Matrix Multiplication (Prerequisite 1)
• 111 Addition of Two Vectors (Prerequisite 2)
• 112 Convergence in Repeated Matrix Multiplication- The Details
• 113 PageRank as a Matrix Operation
• 114 PageRank Explained - Week
• 115 Introduction to Power Law
• 116 Why do Normal Distributions Appear?
• 117 Power Law emerges in WWW graphs
• 118 Detecting the Presence of Power Law
• 119 Rich Get Richer Phenomenon
• 120 Summary So Far - Model)-1 Implementing Rich-getting-richer Phenomenon (Barabasi-Albert - Model)-2 Implementing Rich-getting-richer Phenomenon (Barabasi-Albert
• 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) - Week
• 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
  • problem statement) Analysis of basic reproductive number in branching model (The
• 139 Analyzing basic reproductive number
• 140 Analyzing basic reproductive number
• 141 Analyzing basic reproductive number
• 142 Analyzing basic reproductive number - Week
• 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
  • Week
• 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 comparison 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

How do you find your dream job is obesity contagious? Do you think a friend’s friend

whom you do not know has any influence at all in your life?

(Refer Slide Time: 00:22)

(Refer Slide Time: 00:30)

And what has these questions to do with let say how google works? What are the

commonalities across these questions? Are there any commonalities in the first place?

Well yes, that is what makes the subject called social networks. So, we will be studying

all these questions and more throughout the course without any further ado let us start off

with a nice question. So, we are going to watch a video clip right now, I am going to

come back and then analyze what just happened in the video clip.

Hey.

Hey Ahmed, where were you? Class is about to start.

Leave it, do you even know Vardan and Simran are dating and for the Heaven’s sake this

is just the second day of our college.

How did you come to know about that?

You do not know? Everyone knows about it.

How is that even possible? Anamika told me about it yesterday and that too personally

and I just told Harita about it.

Harita! Who is Harita? I do not know about her.

As far as I know she does not talk to many people here. It has been just one day since we

have met and I am still wondering; how did you come to know about that?

Of course, not many of us know each other and we have met each other yesterday only

and that too only few of us interacted.

I see some signs going around here.

Let me revise it. So, we are the bunch of people who have not met each other before and

then met each other yesterday only and only few of us interacted and still.

And yet everyone knows about the news, Anamika told me personally.

Indeed only few of us know each other still we are so connected.

(Refer Slide Time: 03:11)

They are just less than one week into the class in the college and for everybody to know

this piece of gossip they all must be friends with each other, is it not, without being

friends with each other, who will come and tell each other about all these things? They

may not even be Facebook friends this early, right. It has been less than a week since

they have joined the college what is happening here, let us analyze.

(Refer Slide Time: 03:30)

We need to develop a few pre requisites before we can answer this question. So, let me

go slowly, the classroom let us say has some 50 people, 50 people may not be friends

with each other because as I told you it is just the first few days of the class, let me try

modeling this these are the 50 friends let me use dots to denote these friends and the

friendships between them let me draw a line to denote the friendship by this I mean.

(Refer Slide Time: 04:09)

Assume Rama and Krishna are part of this classroom and they are friends with each

other I put a line between them and Krishna and Ramesh are friends with each other I put

this line between them and, but Rama and Ramesh are not friends. So, I do not put line

between them and I develop the friendship network I call this friendship network.

It might look something like this some points denoting people and lines denoting friends.

There are different ways in which people call this dots are called vertices or nodes in the

subject and the lines are called edges or links. So, what we will be doing is we will we

are going to use the jargon vertices or nodes for dots, edges or links for lines from now

onwards throughout the course that is with the nomenclature.

(Refer Slide Time: 05:58)

The final graph I am going to call it as structure a graph the final graph might look like

this what is surprising about this graph? The surprising fact is that while a person can

actually make all 50 people as his friends the entire class as his friends 49 to be precise

excluding him. Let say he just makes 3 people as friends why because it is the beginning

of the course and what do I observe here in this graph what is startling for me is that this

graph this network is connected what do I mean by connected.

You see take any 2 people here in this graph there is a path that connects these 2 people

this is strange is this always true not really.

(Refer Slide Time: 07:00)

Observe this particular example where there are 50 people, the 20 people, this side 25

people that side they have some friendships within, but there is no friendships across this

might also happen in such a case I do not call this graph connected.

(Refer Slide Time: 07:32)

Why because I can take a person from this end and a person from that end there is not

path connecting these 2 people, but then look at my previous graph on an average if I

make 3 friends per person by picking these 3 people uniformly at random I observe that

the graphs gets connected is this always true.

that side and a point to note is there is no friendship across my question was is this even

possible.

(Refer Slide Time: 08:46)

So, let me do one thing, I will now take a break, write a piece of program and get back to

you people and tell you my observation of course, I will not show you the program, I will

go do the program and came back and tell you the output of the program.

Social Networks Prof. S. R. S. Iyengar Department of Computer Science Indian Institute of Technology, Ropar

Lecture - 02 Introduction to Social Networks Answer to the puzzle

(Refer Slide Time: 00:05)

Fine, I am back. So, I did this piece of programming, what did I do? I took 50 dots, for

each dot I randomly chose 3 people.