Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Demystifying LinkedIn using probabilities

Tavish Srivastava
Tavish Srivastava Last Updated : 18 Apr, 2015
4 min read

top imageYou will be able to view only 1-2 Million of profiles out of 18 Million profile on LinkedIn,

if you are a non premium customer

Since last 3 years, I spend more than half an hour on LinkedIn everyday. This is what corporate life does to us. LinkedIn is a one stop solution for almost anything you want to know in this corporate environment. I use LinkedIn for reading interesting article, knowing interesting people, marketing etc. Here is a restriction LinkedIn puts on any non-premium users :

[stextbox id=”grey”] You are not allowed to see full profile of some of the restricted 3rd level friends

(neither friends nor friend of friends) [/stextbox]

This article will give you a flavor of how you can use probability to think beyond what is visible to an average user in any social networking website. Here is simple tricks using probabilities on LinkedIn to overrule the restriction put by LinkedIn on its non premium users.

[stextbox id=”section”] Sizing of 3rd/3rd+ level locked Linkedin friend : [/stextbox]

Let’s start with estimating some numbers.

Total number of friends I have on my LinkedIn network = 1,700

Total number of 2nd degree friends on my LinkedIn network = 853,430

Total number of group users (share a group) = 973,323

The total of these numbers is the total number of profiles open to me to visit. I am likely to get profile restriction message for all other users. Let’s do a sizing of this number.

Say total number of users on LinkedIn = N

Number of friends of Mr.X = 10482

Number of common friends between Mr. X & Tavish Srivastava = 1

Say,

Probability of a person being a friend of Tavish = a = 1700/N

Probability of a person being a friend of Mr.X = b = 10482/N

Probability of a person being a friend of Mr.X & Tavish = c = 1/N

For independent  events,

a * b = c

N = 1700 * 10482 / 1 = 17.8 Million

Number of profiles not open to me = 17.8 MM – 1.8 MM = 16 MM

[stextbox id=”grey”]

~16MM profiles on LinkedIn are not open to me because LinkedIn thinks I do not know the person enough

[/stextbox]

[stextbox id=”section”]  2 steps to unlock details of 3rd/3rd+ level Linkedin friend : [/stextbox]

LinkedIn restrict users to view a profile if it thinks you don’t know the person enough. This is the screen you getlink1

We will like to open the full profile of this person without upgrading our account to premium. Let’s try to understand what factors linkedin takes into account before deciding whether a person X knows Y.

[stextbox id=”grey”]

Here are the behavioral attributes I can think of :

1. Number of common friends between X and Y

2. Number of common communities between X and Y

3. Profile matches between X and Y (University, company)

4. Any specific information X knows about Y ( like Email ID)

5. Last page viewed by X before landing to page of Y

[/stextbox]

4 out of the 5 attributes of this list are very difficult to trigger unless until X actually knows Y. Last criterion is something one can easily manipulated. Here’s a simple way to do the same.

X reaches a Y’s profile but Y’s profile is locked. LinkedIn has a tab on left hand side called “people also viewed”. Consider following event :

P(A) : Random person views Y’s profile

P(B) : Random person views B’s profile.

B’s name comes in Y’s  “people also viewed” tab 1st name. Hence, P(B/A) is extremely high.

We all know that P(B/A) ~ P(A/B) or both move in the same direction. If P(B/A) is high P(A/B) will also be high.

[stextbox id="grey"]
Step 1 : X clicks on B's icon in the Y's "people also viewed tab 
(As illustrated in the following figure) 
[/stextbox]

link2

Hence, if X goes into B’s profile from this page, it is highly likely that X will be on B’s “people also viewed” tab. Also because X reaches B through Y’s profile and not a random search, he triggers the 5th criterion (among the 5 attributes identified above) and will be able to see complete B’s profile.  

X finds Y on 4th number in the B’s “people also viewed” tab.

[stextbox id="grey"]
Step 2 : X clicks on Y's icon in the B's "people also viewed tab
(As illustrated in the following figure) 
[/stextbox]

link3

This time however X triggers 5th criterion for Y as well and hence linkedin shows X Y’s complete profile.

link5

[stextbox id=”section”] End Notes : [/stextbox]

The algorithm works in most of the cases, however, in some cases you might not find the target in the list of  “people also viewed” of the intermediate person. This is probably because you chose an intermediate person who might be much more popular than target person. In such cases try choosing people who have a closer relation to the target person. Please note that the article is based on my experience with analytics and frequent usage of LinkedIn, there is a possibility that we might have missed some of the variables LinkedIn takes into account. Do let us know of any attribute you think might be one of the variable LinkedIn takes into account and we missed to mention the same in this article.

Did you find the article useful? Share with us any other techniques which you know to make use of social networks in a better way. Also share with us any live examples of using probabilities in real world scenarios. Do let us know your thoughts about this article in the box below.

If you like what you just read & want to continue your analytics learning, subscribe to our emails or like our facebook page.

applicationBusiness Analyticsconditional probabilitylinkedinprobability
Tavish Srivastava
Tavish Srivastava

Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of diverse experience in markets including the US, India and Singapore, domains including Digital Acquisitions, Customer Servicing and Customer Management, and industry including Retail Banking, Credit Cards and Insurance. He is fascinated by the idea of artificial intelligence inspired by human intelligence and enjoys every discussion, theory or even movie related to this idea.

Free Courses

Generative AI 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
Large Language Models 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
Prompt Engineering 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
LlamaIndex 4.8

Improving Real World RAG Systems: Key Challenges & Practical Solutions

Explore practical solutions, advanced retrieval strategies, and agentic RAG systems to improve context, relevance, and accuracy in AI-driven applications.
RAG systems using LlamaIndex 4.7

Microsoft Excel: Formulas & Functions

Master MS Excel for data analysis with key formulas, functions, and LookUp tools in this comprehensive course.

Recommended Articles

Responses From Readers

Clear

Umesh
Umesh

Tavish, You have stated that N is the total number of Linkedin users, but I guess that assumption is wrong because if it is so, N keep changing based on the number of friends that I have or Mr.X and its not true.. The total number of linkedin users will not change based on the friends that individual have, But N can be statednas the number of people within my network

123

Tavish Srivastava
Tavish Srivastava

Umesh, Thats a fair question.There are multiple factors influencing the number n. One of which as you rightly identified is no. of my friend. In case i increase my number of friend by say 100% , there is a probability number of common friends increase as well. Say number of common friend also increase to 2 . If you do the same calculation you will find the same number 18 MM. However, there is a small scope of inaccuracy in this case. If the number of common friends were more than 50, this calculation would have been more accurate. Hope this clarifies your query. Let us know your thoughts on the same. Tavish

123

Sanjay
Sanjay

Great hack, it works!

123

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag
Congratulations, You Did It!
Well Done on Completing Your Learning Journey. Stay curious and keep exploring!
Behind the Screen: How Netflix Uses Data Science?
Behind the Screen: How Netflix Uses Data Science?
Food for thought: How to Measure Influence in a Network?
Food for thought: How to Measure Influence in a Network?

We use cookies essential for this site to function well. Please click to help us improve its usefulness with additional cookies. Learn about our use of cookies in our Privacy Policy & Cookies Policy.

Show details