Graph Neural Network from Udemy

In recent years, Graph Neural Network (GNN) has gained increasing popularity in various domains due to its great expressive power and outstanding performance. Graph structures allow us to capture data with complex structures and relationships, and GNN provides us the opportunity to study and model this complex data representation for tasks such as classification, clustering, link prediction, and robust representation.

While the first motivation of GNN's roots traces back to 1997, it was only a few years ago (around 2017), that deep learning on graphs started to attract a lot of attention.

Since the concept is relatively new, most of the knowledge is learned through conference and journal papers, and when I started learning about GNN, I had difficulty knowing where to start and what to read, as there was no course available to structure the content. Therefore, I took it upon myself to construct this course with the objective of structuring the learning materials and providing a rapid full introductory course for GNN.

This course will provide complete introductory materials for learning Graph Neural Network. By finishing this course you get a good understanding of the topic both in theory and practice.This means you will see both math and code.

If you want to start learning about Graph Neural Network, This is for you.

If you want to be able to implement Graph Neural Network models in PyTorch Geometric, This is for you.

What's inside

Learning objectives

Graph representation learning
Graph neural network (gnn)
Graph analysis
Graph embedding
Deepwalk
Node2vec
Graph convolution network (gcn)

Graph attention network (gat)
Simplifying graph convolution (sgc)
Inductive and transudative learning
Graphsage
Pytorch geometric
Convolution

Graph representation learning
Graph neural network (gnn)
Graph analysis
Graph embedding
Deepwalk
Node2vec
Graph convolution network (gcn)
Graph attention network (gat)
Simplifying graph convolution (sgc)
Inductive and transudative learning
Graphsage
Pytorch geometric
Convolution

Syllabus

By the end of this section you will learn: 1- Definition of graph, 2- How to store graph data , 3- Graph terminologies such as degree and Laplacian, 4- Definition of learning in Graph, 5- Motivation

Graph Definition

Storing Graph Information

Graph Degree and Laplacian of Graph

Definition of Learning in Graph Representation Learning

Drawback in existing graph learning models

Workshop - Using Torch and Torch Geometric for defining a graph

By the end of this section you will learn about:1-Convolution operation from Neural Network and Signal processing point-of-view, 2-How to apply convolution to graph data, 3-Message Passing Framework

Review on Convolution Operation

Graph Convolution (Signal Processing Point of View) Part A

Graph Convolution (Signal Processing Point of View) Part B

Message Passing Framework

In this Section you will learn about: Graph Embedding methods such as: DeepWalk, Node2Vec, Simplifying Graph Convolution (SGC), Graph Convolution Network (GCN), Graph Attention Network (GAT)

Graph Embedding Problem Statement

DeepWalk Algorithm

Workshop - RandomWalk using karateclub library

Node2Vec Algorithm

Workshop - Node2Vec Using Karateclub

Workshop - Node2Vec Using Pytorch Geometric (Part A)

Workshop - Node2Vec Using Pytorch Geometric (Part B)

GNN Motivation

Simplifying Graph Convolution Network

Workshop - SGC (Part A)

Workshop - SGC (Part B)

Graph Convolution Network (GCN)

Graph Attention Network

By the end of this section you will learn about: 1- Definition of Inductive and Transductive Learning 2-GraphSAGE Algorithm

Review on Popular GNN Embedding Methods

Transductive vs Inductive Embedding Methods

GraphSAGE

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides introductory materials for learning Graph Neural Networks, making it suitable for those new to the field

Covers both the theoretical and practical aspects of Graph Neural Networks, which is helpful for a comprehensive understanding

Includes workshops using PyTorch Geometric, which is a valuable tool for implementing GNN models

Explores graph embedding methods like DeepWalk and Node2Vec, which are fundamental techniques in graph representation learning

Discusses inductive and transductive learning methods, which are important concepts for generalizing GNN models to new data

Requires familiarity with PyTorch Geometric, which may pose a barrier for learners without prior experience with this library

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Graph Neural Network with these activities:

Review Linear Algebra Fundamentals

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Solidify your understanding of linear algebra concepts, which are crucial for understanding graph embeddings and the mathematical foundations of GNNs.

Browse courses on Linear Algebra

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Review matrix operations such as multiplication and transposition.
Practice solving systems of linear equations.
Understand eigenvalues and eigenvectors and their significance.

Brush Up on Neural Network Basics

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Revisit the fundamentals of neural networks, including backpropagation and activation functions, to better grasp the architecture and training of GNNs.

Browse courses on Neural Networks

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Review the structure of a basic neural network.
Understand the concept of backpropagation and gradient descent.
Familiarize yourself with common activation functions.

Form a Study Group

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Collaborate with peers to discuss course concepts, work through examples, and clarify any misunderstandings related to graph theory and GNN architectures.

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Find classmates interested in forming a study group.
Schedule regular meetings to discuss course material.
Collaboratively work through practice problems and examples.

Four other activities

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Implement Graph Traversal Algorithms

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Practice implementing graph traversal algorithms like Breadth-First Search (BFS) and Depth-First Search (DFS) to solidify your understanding of graph structures.

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Implement BFS on a sample graph using Python.
Implement DFS on the same graph and compare the results.
Experiment with different graph representations (adjacency list, adjacency matrix).

Read 'Graph Representation Learning'

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Expand your knowledge of graph representation learning with a comprehensive book covering various GNN architectures and their applications.

View Graph Representation Learning on Amazon

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Obtain a copy of 'Graph Representation Learning' by William L. Hamilton.
Read the chapters related to GCNs, GraphSAGE, and GATs.
Take notes on key concepts and algorithms.

Build a Simple GNN for Node Classification

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Apply your knowledge by building a basic GNN model using PyTorch Geometric to classify nodes in a small graph dataset.

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Choose a small graph dataset (e.g., Cora, CiteSeer, PubMed).
Implement a simple GCN layer using PyTorch Geometric.
Train and evaluate your GNN model on the dataset.

Explore Advanced GNN Architectures

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Deepen your understanding by exploring tutorials and implementations of more advanced GNN architectures like GraphSAGE and GAT.

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Find tutorials on implementing GraphSAGE and GAT using PyTorch Geometric.
Follow the tutorials to implement these architectures.
Compare the performance of these architectures on different datasets.

Career center

Learners who complete Graph Neural Network will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer develops and deploys machine learning models. These models often rely on complex data structures and relationships. This course on Graph Neural Networks is directly relevant, as GNNs are powerful tools for capturing and modeling complex data for tasks like classification and prediction. The course's focus on graph representation learning and specific GNN architectures like Graph Convolution Networks and Graph Attention Networks helps build a strong foundation for developing sophisticated machine learning solutions. Furthermore, hands-on experience with PyTorch Geometric, also covered in the course, helps the Machine Learning Engineer implement and experiment with GNN models.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist analyzes data to extract insights and build predictive models. Graph Neural Networks enable the analysis of complex, relational data, making this course highly valuable. The coverage of graph representation learning, including techniques like DeepWalk and Node2Vec, provides a Data Scientist with tools for understanding and leveraging relationships within data. The course's emphasis on both theory and practice, along with its exploration of GNN architectures like Graph Convolution Network and Graph Attention Network, helps build the skills needed to apply GNNs to real-world problems. The practical workshops using PyTorch Geometric enhances the Data Scientist's ability to implement and experiment with these models.

See salaries and explore the career path for Data Scientist

AI Application Developer

An AI Application Developer is responsible for building and deploying applications that leverage artificial intelligence. This course on Graph Neural Networks directly supports this role by teaching the fundamentals of GNNs, which can be used for a variety of AI applications such as node classification, link prediction, and graph classification. The course's emphasis on both theory and practical implementation, using tools like PyTorch Geometric, helps equip someone entering the AI Application Developer role with the skills needed to integrate GNNs into real-world applications.

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Machine Learning Researcher

A Machine Learning Researcher investigates and develops new machine learning algorithms and techniques. Machine learning researchers often require an advanced degree. Graph Neural Networks are an active area of research in machine learning, and this course offers an introduction to the field. The curriculum covers foundational concepts in graph representation learning and explores various GNN architectures. For someone interested in becoming a Machine Learning Researcher, the course may bridge the gap between academic papers and practical implementation, and helps build a foundation for more in-depth research. The course emphasizes the practical aspects of GNNs, including the use of PyTorch Geometric, which may be especially useful.

See salaries and explore the career path for Machine Learning Researcher

Research Scientist

A Research Scientist designs and conducts experiments, and analyzes data to advance scientific knowledge. Graph Neural Networks are a powerful tool for modeling complex relationships, and this course provides a solid introduction to the field. The curriculum covers foundational concepts in graph representation learning and explores various GNN architectures, such as Simplifying Graph Convolution and GraphSAGE. For someone interested in becoming an Research Scientist, the course may bridge the gap between academic papers and practical implementation, and helps build a foundation for more in-depth research. The course emphasizes the practical aspects of GNNs, including the use of PyTorch Geometric, which may be especially useful.

See salaries and explore the career path for Research Scientist

Artificial Intelligence Researcher

An Artificial Intelligence Researcher investigates and develops new AI algorithms and techniques. Graph Neural Networks are an active area of research in AI, and this course offers a solid introduction to the field. The curriculum covers foundational concepts in graph representation learning and explores various GNN architectures, such as Simplifying Graph Convolution and GraphSAGE. For someone interested in becoming an Artificial Intelligence Researcher, the course bridges the gap between academic papers and practical implementation, and helps build a foundation for more in-depth research. The course emphasizes the practical aspects of GNNs, including the use of PyTorch Geometric, which may be especially useful.

See salaries and explore the career path for Artificial Intelligence Researcher

Deep Learning Engineer

A Deep Learning Engineer designs and implements deep learning models for various applications. Graph Neural Networks are a type of deep learning model, and this course provides a solid introduction to the field. The curriculum covers foundational concepts in graph representation learning and explores various GNN architectures, such as Simplifying Graph Convolution and GraphSAGE. For someone interested in becoming a Deep Learning Engineer, the course may bridge the gap between academic papers and practical implementation, and helps build a foundation for more in-depth research. The course emphasizes the practical aspects of GNNs, including the use of PyTorch Geometric, which may be especially useful.

See salaries and explore the career path for Deep Learning Engineer

AI Consultant

An AI Consultant helps organizations understand and implement AI solutions. This course on Graph Neural Networks may be useful for providing the consultant with a foundational understanding of GNNs and their potential applications. The course's coverage of various GNN architectures and graph representation learning techniques may help the AI Consultant advise clients on the suitability of GNNs for their specific needs. The familiarity with PyTorch Geometric, gained through the course's practical workshops, could enable the AI Consultant to demonstrate the capabilities of GNNs through proof-of-concept implementations.

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Data Analyst

A Data Analyst collects, processes, and performs statistical data analysis. This course on Graph Neural Networks may be useful, as it provides the tools to analyze and understand complex network structures. The course's coverage of graph representation learning, including techniques like DeepWalk and Node2Vec, offers methods for embedding network data into a format suitable for analysis. The exploration of GNN architectures such as Graph Convolution Network and Graph Attention Network may allow the Data Analyst to extract meaningful insights from network data. Additionally, the course's focus on PyTorch Geometric may help with implementing and experimenting with GNNs for data analysis.

See salaries and explore the career path for Data Analyst

Network Analyst

A Network Analyst studies and models networks, such as social networks or communication networks. This course on Graph Neural Networks may be useful, as it provides the tools to analyze and understand complex network structures. The course's coverage of graph representation learning, including techniques like DeepWalk and Node2Vec, offers methods for embedding network data into a format suitable for analysis. The exploration of GNN architectures such as Graph Convolution Network and Graph Attention Network may allow the Network Analyst to extract meaningful insights from network data. Additionally, the course's focus on PyTorch Geometric may help with implementing and experimenting with GNNs for network analysis.

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Bioinformatics Scientist

A Bioinformatics Scientist analyzes biological data, often involving complex relationships between genes, proteins, and other biological entities. Graph Neural Networks may be useful for modeling these relationships, and this course provides an introduction to GNNs. The coverage of graph representation learning and GNN architectures like Graph Convolution Networks and Graph Attention Networks may allow the Bioinformatics Scientist to analyze biological networks and predict interactions. Hands-on experience with PyTorch Geometric may help the Bioinformatics Scientist implement and customize GNNs for specific bioinformatics applications.

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Data Engineer

A Data Engineer builds and maintains the infrastructure for data storage and processing. While this course focuses on modeling and analysis, an understanding of Graph Neural Networks may be useful for Data Engineers working with graph-structured data. The knowledge of graph representation learning techniques, such as DeepWalk and Node2Vec, may help inform decisions about data storage and retrieval. The course's exploration of PyTorch Geometric may provide insights into how GNNs can be integrated into data pipelines. This is especially true if the Data Engineer works at a company that uses data science and machine learning to do its business.

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Natural Language Processing Engineer

A Natural Language Processing Engineer develops algorithms that allow machines to understand and process human languages. While Natural Language Processing primarily deals with text, this course on Graph Neural Networks may be useful, as it could provide methods for representing relationships between words and concepts in a knowledge graph. The graph embedding techniques covered, such as DeepWalk and Node2Vec, may allow the engineer to represent words as nodes in a graph and analyze their semantic relationships. The practical experience with PyTorch Geometric, also provided in the course, may enable the engineer to experiment with GNNs for specific natural language processing applications.

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Computer Vision Engineer

A Computer Vision Engineer develops algorithms and systems that enable computers to “see” and interpret images. While seemingly distant, this course on Graph Neural Networks may offer tools for representing and analyzing relationships within images, especially in tasks like scene understanding and object detection where context matters. The graph embedding techniques covered, such as DeepWalk and Node2Vec, may allow the engineer to represent image features as nodes in a graph and analyze their relationships. The practical experience with PyTorch Geometric, also provided in the course, may enable the engineer to experiment with GNNs for specific computer vision applications.

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Software Developer

A Software Developer designs, develops, and tests software applications. This course on Graph Neural Networks may be useful for Software Developers working on applications that involve graph-structured data. The course's emphasis on practical implementation with PyTorch Geometric provides skills in building and integrating GNN models into software. The understanding of graph representation learning and GNN architectures may help build more efficient and intelligent software solutions. Furthermore, the software that the Software Developer builds may be used at a company that focuses on data driven insights.

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Graph Neural Network

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