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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 Graph Database?

Ayushi Trivedi
Ayushi Trivedi Last Updated : 30 Aug, 2024
8 min read

Introduction

As data scales and characteristics shift across fields, graph databases emerge as revolutionary solutions for managing relationships. Unlike relational databases that use tables and rows, graph databases excel in handling complex networks. Imagine a social network where members connect as friends, followers, or colleagues—graph databases shine in such interconnected data scenarios. This article provides an overview of graph databases, highlighting key terminology, benefits, and their role in revolutionizing data management.

What is Graph Database?

Overview

  • Understand what a graph database is and how it differs from traditional relational databases.
  • Learn about the core components and architecture of graph databases.
  • Explore the advantages and use cases of graph databases.
  • Gain insights into how to effectively implement and query graph databases.
  • Be able to identify common graph database technologies and their applications.

Table of contents

What is a Graph Database?

Graph database is used to store and search data that is in a state of connection between the elements. Whereas Relational database stores data in a tabular structure of rows and columns with relations between fields defined as keys, Graph database, stores data in the form of graph structures. This structure consists of nodes which are the entities, edges- the relationships and properties- the attributes of the entities incorporated in constructing a dynamic map of data.

What is a Graph Database?
  • Nodes: They are the major building blocks of a these database. They depicts individuals, companies or even a product. Every node may include a set of characteristics referred to as properties. For instance, if the node is a ‘Person’ the attributes may be name, age, email.
  • Edges: Edges are the lines which connect two nodes and they represent the relations between the entities. It can be directed ( pointing to a one Single form of relationship), or undirected ( pointing to two forms of relationship). Edges can of course also have attributes that characterise the nature of the relationship, such as “friend” or “colleague.”
  • Properties: Extra information about nodes and edges are given by properties. It is just a key-value pair which supplement the information which can be extracted from the graph. For instance, a node that represents a product can have attributes such as price or manufacturer while a link between to nodes can encompass a label that read “purchased by”.

Core Components and Architecture

Let us learn about core components of graph database.

  • Nodes: Nodes are the primary units in a these database, representing entities. Each node can store various attributes and be connected to other nodes through edges. Nodes form the vertices of the graph, and their connections define the structure of the graph.
  • Edges: Edges are the connections between nodes that illustrate relationships. They can be directed, showing a one-way relationship, or undirected, indicating a two-way connection. Edges are essential for traversing the graph and performing queries based on relationships.
  • Properties: Properties add context and detail to both nodes and edges. They consist of key-value pairs that provide additional information, such as a person’s date of birth or the date a transaction occurred.
  • Graph Algorithms: They support various algorithms designed to analyze and traverse the graph structure. These include algorithms for finding the shortest path between nodes, identifying key influencers, and detecting communities or clusters within the graph.

Use Cases of Graph Database

Graph databases excel in various domains where understanding and managing relationships are crucial.

Social Networks

In social networks, graph databases help manage intricate connections between users, such as friendships, followers, and interactions. They enable efficient queries that can analyze social graphs, uncover patterns, and provide insights into user behavior and network dynamics. For instance, Facebook uses graph databases to manage user connections and recommend friends based on shared interests and mutual friends.

Fraud Detection

In fraud detection, graph databases involves data analysis on transactions and its relation to other entities with a purpose of identifying fraudulent acts. In this way, these databases are much more effective at finding discrepancies and possible fraudulent data, than using simple approaches. For instance, the graph database can be used in the financial institutions to accomplish the following; recognize a number of accounts that are toxic and comprise fraudulent activities such as money laundering.

Recommendation Systems

In recommendation systems, graph databases support personalized recommendations by analyzing user preferences and their relationships with other users or products. This allows for more accurate and relevant suggestions based on complex patterns of behavior and interactions. Streaming services like Netflix use graph databases to analyze user viewing habits and suggest content that aligns with their interests.

Network Management

Network management gains from graph databases since it offers tools that can be used in examining network topology and even in improving it depending on the network involved, this can apply to the telecommunication or any computing network. They assist in determination of the actual shape of the network, that is, whether it is centralized or decentralized, determination of the areas of congestion within the network and enhancement of the network performance. For example, telecom companies utilise graph databases to govern and/or control their networks which enables them to have effective flow of information within a limited time without disruptions.

Common Graph Database Technologies

Let us now look into the common graph database technologies.

Neo4j

Neo4j is one of the most used graph databases because of its reliability and rich set of tools available. It relies on Cypher query language which effectively helps in simplification of composite queries and is effective in traversal of graphs. There are a number of applications of Neo4j include in social networks, recommendation engines and many more. Some of the additional features that make it a great solution for the enterprises are its ACID compliant transactions and integrated graph solutions.

Amazon Neptune

AWS’s managed graph database service supports both property graph and RDF graph models. It offers high availability and scalability, making it suitable for various applications, including knowledge graphs and complex query processing. Neptune integrates seamlessly with other AWS services, providing a comprehensive solution for building graph-based applications on the cloud.

ArangoDB

ArangoDB is designed as multi-model database for graph, document and key-value data models. Due to its flexibility, it means that it can be used for different purposes, and flexibility in handling the data. The features of graph in ArangoDB include the capability to perform different graph algorithms as well as optimized query system recommendation for multi-model data application.

OrientDB

OrientDB is the system built on the basis of document and graph databases. It has capabilities for performing graph DBMS as well as document DBMS to make it an all-round option for applications which need both. Due to OrientDB’s ability to use NoSQL data schemas and enhanced graph functionality, it is optimal for complicated and dynamic datasets.

Implementing Graph Databases

Implementing a graph database involves several steps and considerations to ensure successful deployment and integration. Here’s a general guide to the process:

Step1: Define Requirements

Start by identifying the specific needs and objectives of your application. Determine the types of data you need to store, the relationships you need to model, and the queries you need to perform. This will help in selecting the right graph database technology and designing the schema.

Step2: Choose a Graph Database

Based on your requirements, select a graph database technology that best fits your needs. Consider factors such as scalability, performance, ease of use, and compatibility with your existing infrastructure.

Step3: Design the Schema

Design the schema for your graph database, including the nodes, edges, and properties. Ensure that the schema aligns with your data requirements and allows for efficient querying and traversal.

Step4: Data Migration

If you are migrating from a relational database or another data source, plan the data migration process. This involves transforming your data into a graph format and loading it into the graph database. Data migration tools and ETL (extract, transform, load) processes can facilitate this step.

Step5: Optimize Queries

Optimize your queries to ensure they perform efficiently. Use indexing and query optimization techniques to improve query performance and reduce response times.

Step6: Monitor and Maintain

Continuously monitor the performance of your graph database and perform regular maintenance tasks. This includes updating the schema as needed, managing data growth, and ensuring data integrity.

Step7: Integration

Integrate the graph database with your application and other systems. Ensure that the database interacts seamlessly with your application logic and provides the necessary data for your use cases.

    Advantages of Graph Databases

    We will now explore the advantages of graph databases.

    • Effective Relationship Management: These are optimized for handling and querying complex relationships. This makes them particularly useful for applications like social networking, where the connections between users are as important as the individual user data.
    • Schema Flexibility: Unlike relational databases, which require a fixed schema, graph databases offer flexibility in schema design. This allows for easier adaptation to changes in data structure and requirements.
    • Real-time Processing: The ability to traverse and analyze relationships quickly enables real-time processing and insights, making these databases suitable for applications that require immediate analysis of complex data.
    • Intuitive Querying: Specialized query languages such as Cypher (for Neo4j) and Gremlin (for Apache TinkerPop) allow for expressive and straightforward querying of graph data. These languages are designed to handle complex queries involving relationships and connections.

    The field of graph databases is evolving rapidly, with several trends shaping the future of this technology:

    • Enhanced Scalability: While graph databases are being used in increasing bigger and more versatile applications, more attention is being paid to increasing scalability. Further enhancements are expected to be witnessed in more complex distributed architecture and improved horizontal scalability for the management of large data and relations.
    • Integration with Machine Learning and AI: The usage of this databases is rising with Machine learning and AI-based technologies. This integration enables one to perform sophisticated analyses, predictive modeling, and improve decision making based on the relations and the patterns deduced out of graph data.
    • Improved Query Languages: It is for instance possible for future developments to add enhancements to query languages or advance query language systems on existing ones. Many of these enhancements will be designed to further refine and enhance the ease of use and functionality of graph data views and contexts with regard to querying and structure traversal.
    • Hybrid Data Models: It was noted that the continued evolution of graph databases is going to be complemented with the use of other models such as document or key-value stores in combination with the graph DBMS. This approach helps one achieve more flexibility as well as deal with various types of data and applications.
    • Increased Cloud Adoption: It is expected that the use of graph databases in cloud systems will continue to grow due to applications’ scalability, growth of managed services, and combining possibilities with other cloud-related solutions. They will be integrated with more capabilities by cloud providers and more improved features will be availed to users.

    Challenges and Considerations

    While graph databases offer many advantages, there are also challenges and considerations to keep in mind:

    • Performance and Scalability: There are some issues that have to do with performance and scalability when the size of the graph housing the data to be queried increases and when the queries is complex. In this context, it is important to guarantee that a graph data base is capable of processing a huge amount of data and queries, and this must be considered from the design perspective.
    • Data Modeling Complexity: The process of how to design a graph schema is not an easy task, mainly for big and highly changing datasets. It has to be carefully worked out in terms of the data and its organization in order to properly reflect the data that will be queried and analyzed.
    • Integration with Existing Systems: When implementing a graph database in an organization’s environment that utilizes other systems based on different data models. This is why integration must be planned and perhaps even developed uniquely, to guarantee that the integration process goes smoothly.
    • Data Consistency and Integrity: Ensuring consistency and data accuracy in a graph-based approach and specifically in a distributed setting, the transactions management becomes inevitably essential.
    • Skill and Expertise: To work with such databases one has to have some theoretical knowledge and experience in graph theories, query language, use of DBMS, etc. There is likely to be the need to train some personnel or hire experts, especially where an organization intends to fully leverage on the graph databases.

    Conclusion

    Graph databases are fundamentally a revolution in the method of data management and processing are the most useful in managing relationships. Due to their naturalness, versatility of the schema, and querying capacity they are essential tools for a wide range of application areas including social nets or fraud Tack. Since data remains a complex and developing asset, Graph databases will also remain a vital aspect in the discovery and fostering of new value propositions.

    Frequently Asked Questions

    Q1. What are the main advantages of using a graph database?

    A. They excel in handling complex relationships, offering flexibility in schema design, enabling real-time analytics, and providing intuitive querying capabilities.

    Q2. How do graph databases differ from relational databases?

    A. They focus on the relationships between entities, using nodes and edges, while relational databases use tables and rows to store data. They are also more efficient for managing interconnected data.

    Q3. What are some common use cases for graph databases?

    A. Common use cases include social networks, fraud detection, recommendation systems, and network management.

    Q4. What are some popular graph database technologies?

    A. Popular graph database technologies include Neo4j, Amazon Neptune, ArangoDB, and OrientDB.

    databasegraphgraph databasesSQL
    Ayushi Trivedi
    Ayushi Trivedi

    My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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