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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

What is Network Analysis?

Yana Khare
Yana Khare Last Updated : 07 Jun, 2024
5 min read

Introduction

Network analysis is essential for examining and interpreting the interactions between elements in complex systems. It is applied in several domains. This approach sheds light on linked systems’ structure, dynamics, and usefulness, whether biological networks, computer systems, or social networks. This article examines the basic ideas, uses, and important network analysis techniques.

Overview

  • Learn about network analysis, which is essential for understanding complex system relationships.
  • Explore the basics of networks fundamental to various domains.
  • Discover network analysis techniques such as centrality measures, community detection, and network visualization.
  • Understand the applications of network analysis in various domains.
What is Network Analysis?

Table of contents

What is a Network?

A network is a collection of entities connected by relationships. In more technical terms, a network is represented as a graph consisting of the following:

  • Nodes (Vertices): A network’s basic units or points. They can represent anything from people in a social network to computers in a network system to genes in a biological network.
  • Edges (Links): These are the links or connections between nodes. They indicate relationships or interactions, such as friendships, data transfers, or regulatory effects.

Types of Networks

  • Undirected Networks: Connections between nodes do not have a direction. For example, friendships in a social network are mutual. Hence, they are typically represented as undirected.
  • Directed Networks: Connections have a direction, representing a one-way relationship like a Twitter follow or a one-way street.

Properties of Networks

  • Degree: The number of connections a node has. A high-degree node is more central or influential in the network.
  • Path Length: The shortest path between two nodes. Shorter paths indicate more direct relationships.
  • Clustering Coefficient: A measure of how connected a node’s neighbors are to each other. Furthermore, it reflects the network’s tendency to form tightly-knit groups.

What is Network Analysis?

Network Analysis examines the structure and dynamics of networks. This reveals how entities are connected and influence the entire system’s behavior. It involves:

  • Analyzing Relationships: Investigating the connections between nodes and how they affect the network.
  • Identifying Patterns: Detecting regularities or anomalies in the network structure.
  • Understanding Dynamics: Exploring how changes in the network affect its functionality.

Types of Network Analysis Techniques

Various methods are used in network analysis to look at and analyze the behavior and structure of networks. These techniques enable analysts and researchers to get critical insights into the structure and dynamics of networks. Some of the main methods for network analysis are as follows:

Centrality Measures

Centrality measures are crucial in evaluating the importance or influence of nodes within a network. They provide a way to determine which nodes hold significant positions based on various criteria:

  • Closeness Centrality: Evaluation based on closeness centrality measures the closeness of a node to all the other nodes in the global network in terms of shortest paths. Nodes calculated to have a high value of closeness centrality can easily communicate with any other nodes, and hence,

Community Detection

Community detection identifies groups of nodes that maintain denser connections among themselves than the rest of the network. This technique is essential for uncovering the modular structure within a network, often revealing natural divisions or subgroups:

  • Modularity Optimization: This method maximizes the density of connections within communities compared to the density of connections between communities. The algorithm effectively identifies clusters where nodes are more interconnected by optimizing modularity.
  • Spectral Clustering: Spectral clustering uses eigenvalues of matrices derived from the graph to detect clusters. It’s particularly effective in identifying communities by reducing the data’s dimensionality and focusing on the most significant structural features.

Network Visualization

Network visualization tools are indispensable for creating visual representations of networks. They help in highlighting key features such as clusters, node centrality, and overall topology:

  • Gephi and Cytoscape: These tools provide interactive platforms for visualizing complex networks. They allow users to explore the network structure, identify significant nodes, and observe patterns that might not be apparent through numerical analysis alone. Visualization aids in understanding the network’s architecture and identifying potential areas of interest.

Network Motifs

Network motifs are small, recurring subgraphs within a larger network. Identifying these motifs helps in understanding the network’s fundamental building blocks and the recurring patterns that drive its behavior:

  • Significant Subgraphs: Researchers can gain insights into the network’s essential structures and dynamics by identifying and analyzing network motifs. These motifs often represent basic functional units that occur frequently and play critical roles in the network’s overall functionality.

Path Analysis

Path analysis examines the pathways in the network, including the shortest paths and flows, to understand connectivity and communication efficiency:

  • Shortest Paths and Flows: Analyzing the paths within a network reveals how nodes are interconnected and how information or resources flow through the network. It helps understand communication efficiency, identify critical paths, and optimize the network’s performance.

Also Read: All About Popular Graph Network Tools in Python

Applications of Network Analysis

Some applications of network analysis are:

  1. Social Networks: It helps in understanding social dynamics, influence spread, and community formation within social media or organizational structures .
  4. Economic and Financial Networks: Network analysis can depict and even forecast the sentiment of a market or a group of interconnected markets, the spread of financial risks or crises, and dependencies between different economies.

Conclusion

Network analysis serves as a cornerstone for understanding complex systems by analyzing the relationships and interactions between entities. This technique transcends traditional data analysis by focusing on the connections within a network, providing valuable insights into the underlying structure, dynamics, and functionality of various interconnected systems.

Network analysis offers a powerful framework for exploring and understanding the intricacies of networks across various domains. Its comprehensive techniques enable the discovery of significant patterns and relationships that traditional methods might overlook. Thus making it an invaluable asset for researchers, analysts, and decision-makers seeking to navigate and leverage the complexities of interconnected systems.

Frequently Asked Questions

Q1. What is the method of network analysis?

A. Network analysis involves examining the structure and dynamics of networks, revealing how entities are connected and influence each other. Methods include centrality measures, community detection, network visualization, and path analysis.

Q2. Why do we study network analysis?

A. We study network analysis to understand the relationships and interactions within complex systems such as social networks, biological networks, and communication networks. It helps uncover patterns, identify key elements, and optimize network performance.

Q3. What are the steps in network analysis?

A. Network analysis steps include data collection, defining nodes and edges, analyzing network properties, applying network analysis techniques, interpreting results, and drawing conclusions.

Q4. What are the benefits of network analysis?

A. Benefits of network analysis include gaining insights into complex systems, identifying influential nodes or communities, optimizing communication efficiency, detecting vulnerabilities, forecasting trends or behaviors, and making informed decisions in various domains such as social sciences, biology, economics, and technology.

Applications of Network AnalysisNetworknetwork analysisTypes of Network Analysis Techniques
Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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Guide to Popular Graph Network Tools in Python
Guide to Popular Graph Network Tools in Python
Community Detection: Getting Started within Graphs and Networks
Community Detection: Getting Started within Graphs and Networks

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