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Home > Data > Data Governance > Applied Deep Learning on Graphs
Applied Deep Learning on Graphs
Applied Deep Learning on Graphs

Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures

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Profile Icon Lakshya Khandelwal Profile Icon Subhajoy Das
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Applied Deep Learning on Graphs

Introduction to Graph Learning

Graph data is a powerful and intuitive way of expressing information, and several practical scenarios can be better expressed using graph data than tabular approaches. Analyzing graph data has been a topic of study for decades, but it has only recently begun to capture the limelight due to advances in compute capabilities.

In this book, we aim to introduce you to the world of graphs. Here, we’ll begin by discussing what graph data is and the fundamental mathematical terminologies surrounding graphs. Next, we’ll take a small detour and discuss some common graph algorithms and their applications in graph data analytics. We’ll extend our discussion on graph data analytics to the requirement of graph deep learning and why it stands as a specialized subdomain compared to applying existing architectures.

In this chapter, we’ll cover the following topics:

  • Do we need graphs?
  • Formalizing graphs
  • Types and properties...

Do we need graphs?

The recent artificial intelligence (AI) revolution is the tip of the iceberg of a megatrend that has been impacting the computing industry for decades now. Over time, computing performance has increased exponentially against power consumed and cost; information storage costs have also decreased exponentially. To put this into perspective, while a terabyte of data can be stored in a disk costing around 100 US dollars in 2024, it would have taken more than a million dollars in the early 1990s!

Using computers and their derivative products, such as software, web applications, games, and multimedia content, has become deeply tied to our normal lifestyle. This dependence led to the need for understanding the behavior of all the interacting entities: humans, computer hardware, software such as web applications, and even organizations as a whole. The end goal was to find ways to make interactions more efficient, which could lead to unprecedented business opportunities...

Formalizing graphs

Graphs are a very popular concept in mathematics. In this domain, a common terminology is well accepted. Let’s take a closer look.

Definition and semantics

With the argument being made for graph representations to be a relevant topic for practical problems, let’s take a moment to define what a graph is. A graph is an abstract concept. Mathematically, it’s generally represented as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>G</mml:mi><mml:mo>(</mml:mo><mml:mi>V</mml:mi><mml:mo>,</mml:mo><mml:mi>E</mml:mi><mml:mo>)</mml:mo></mml:math>, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>G</mml:mi></mml:math> is the graph, which contains a set of vertices, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>V</mml:mi></mml:math>, and a set of edges, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>E</mml:mi></mml:math>. Each element of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>E</mml:mi></mml:math> is a tuple, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>V</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mo>,</mml:mo><mml:mi>V</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>)</mml:mo></mml:math>, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:msub><mml:mrow><mml:mi>V</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mo>,</mml:mo><mml:mi>V</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>∈</mml:mo><mml:mi>V</mml:mi></mml:math>, and represents a connection between the two vertices. That’s all there is to the mathematical definition; how you choose to apply semantics to this is completely up to you.

In the example mentioned in the previous section, the users of the social media platform were represented by the vertices, and the connection between the two users was represented by the edges. Also, vertices and edges need not be so homogeneous. Consider the graph...

Types and properties of graphs

Several types of graphs have been identified, each with its unique properties, but we’ll focus on the ones that are most popular. Note that these types need not be mutually exclusive, meaning a graph can be labeled as more than one type at a time.

Directed graphs

Graphs are directed when the edges have a one-way relationship between their connecting nodes. There are many scenarios where the relationship that’s represented is unidirectional. In a graph representing a family tree, an edge might represent the relation “is a parent of,” and another might represent the relation “is a pet of.” Such relationships can’t be inverted between the nodes and hold the same meaning.

Bipartite graphs

A bipartite graph is a type of graph whose vertices can be divided into two disjoint sets such that every edge connects a vertex from one set to a vertex in the other set. In other words, there are no edges that...

Graph data structures

How should we feed graph data into computer programs so that we can apply graph-based algorithms to solve problems? This will be addressed in this section. Each representation has its advantages and disadvantages, and we’ll explore them from the perspective of the time complexity of determining whether an edge exists and updating the graph.

Adjacency matrix

The adjacency matrix aims to record the graph structure via a matrix. A matrix, say <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>A</mml:mi></mml:math>, of size <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>v</mml:mi><mml:mo>×</mml:mo><mml:mi>v</mml:mi></mml:math> is created (where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>v</mml:mi></mml:math> denotes the number of nodes, or mathematically, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>v</mml:mi><mml:mo>=</mml:mo><mml:mo>|</mml:mo><mml:mi>V</mml:mi><mml:mo>|</mml:mo></mml:math>). We start with all entries of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>A</mml:mi></mml:math> being 0. Next, if <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mo>(</mml:mo><mml:msub><mml:mrow><mml:mi>v</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:msub><mml:mrow><mml:mi>v</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>)</mml:mo><mml:mo>∈</mml:mo><mml:mi>E</mml:mi></mml:math>, then element <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mo>(</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>)</mml:mo></mml:math> of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>A</mml:mi></mml:math> is labeled 1. If the graph is undirected, then if <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mo>(</mml:mo><mml:msub><mml:mrow><mml:mi>v</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:msub><mml:mrow><mml:mi>v</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>)</mml:mo><mml:mo>∈</mml:mo><mml:mi>E</mml:mi></mml:math>, then both elements of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>A</mml:mi></mml:math>, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mo>(</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>)</mml:mo></mml:math>, and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mo>(</mml:mo><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mo>)</mml:mo></mml:math>, are labeled 1.

The time complexity to check whether an edge exists in an adjacency matrix is <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:m="http://schemas.openxmlformats.org/officeDocument/2006/math"><mml:mi>O</mml:mi><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo></mml:math> since it just involves checking a particular cell in the matrix. However, adding a new vertex to the graph would be difficult, and depending on the matrix implementation, it might need...

Traditional graph-based solutions

Many computer scientists have etched their names in history by devising elegant solutions to seemingly complex problems involving graphs. However, graphs aren’t just confined to the algorithm books, and graph-based problems are common in the wild. Lots of business problems and scientific research can be boiled down to graph-based problems, on which existing solutions can be implemented to generate the required output. In this section, we’ll talk about the most popular problems in the domain of graphs, a few approaches to solving them, and where these problems are encountered in practical scenarios.

Searching

There are two fundamental approaches when performing a search over a graph: breadth-first and depth-first. Both are means to traverse a graph from a starting point to all nodes that can be reached from the initial node, but the differentiating factor is their approach.

In BFS, the algorithm explores a graph level by level...

The need for representation learning

Here, we’ll introduce a new concept called representation learning for graphs. Let’s use a small analogy to understand what this means. A typical corporate organization has several entities: employees, IT equipment, offices, and so on. All these entities maintain different types of relationships with each other: employees can be related to each other based on organizational hierarchy; one employee may use several pieces of IT equipment; several pieces of equipment, such as servers, can be networked with each other; employees and equipment can report physically or be located in a particular office, respectively; and so on.

A graph, quite rightly, seems like a natural way to represent this information, like this:

Figure 1.8 – A graph showing the different entities in an organization interacting with each other

Figure 1.8 – A graph showing the different entities in an organization interacting with each other

Graphs are very visually intuitive. However, performing algorithmic calculations on graphs...

GNNs and the need for a separate vertical

We won’t dive into the details of what GNNs do or how they differ from other popular neural network architectures in this chapter. Here, we’ll merely attempt to explain why there’s a need to study GNNs separately from other deep learning architectures.

Before talking about the differences, we must discuss the similarities. GNNs are an architecture choice that’s specialized for processing graph data and outputting representations or node embeddings. Similar to how convolutional networks are fundamental for reading pixel data, the set of architectures under GNNs are optimized for reading graph data. GNN-based learning tasks follow the same trajectory as other deep learning solutions: to iteratively optimize the parameters of the model so that a loss function can be minimized. In the case of GNNs, the loss function often tries to capture and preserve meaningful information about the graph structure.

Now, let&...

Summary

In this chapter, we covered the foundational concepts in graph learning and representation. We began with motivating examples of how graph structures naturally capture relationships between entities, making them a powerful data representation. Then, formal definitions of graphs, common graph types, and key properties were discussed. We also looked at popular graph algorithms such as searching, partitioning, and path optimization, along with their real-world use cases.

A key idea presented here was the need for representation learning on graphs. Converting graph data into vector embeddings allows us to leverage the capabilities of machine learning models. Benefits such as scalability, flexibility, and robustness make graph embeddings an enabling technique.

Finally, we justified the need for specialized GNN architectures. Factors such as irregular structure, permutation invariance, and complex operations such as aggregation and pooling necessitate tailored solutions. GNNs...

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Key benefits

  • Explore graph data in real-world systems and leverage graph learning for impactful business results
  • Dive into popular and specialized deep neural architectures like graph convolutional and attention networks
  • Learn how to build scalable and productionizable graph learning solutions
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

With their combined expertise spanning cutting-edge AI product development at industry giants such as Walmart, Adobe, Samsung, and Arista Networks, Lakshya and Subhajoy provide real-world insights into the transformative world of graph neural networks (GNNs). This book demystifies GNNs, guiding you from foundational concepts to advanced techniques and real-world applications. You’ll see how graph data structures power today’s interconnected world, why specialized deep learning approaches are essential, and how to address challenges with existing methods. You’ll start by dissecting early graph representation techniques such as DeepWalk and node2vec. From there, the book takes you through popular GNN architectures, covering graph convolutional and attention networks, autoencoder models, LLMs, and technologies such as retrieval augmented generation on graph data. With a strong theoretical grounding, you’ll seamlessly navigate practical implementations, mastering the critical topics of scalability, interpretability, and application domains such as NLP, recommendations, and computer vision. By the end of this book, you’ll have mastered the underlying ideas and practical coding skills needed to innovate beyond current methods and gained strategic insights into the future of GNN technologies.

Who is this book for?

For data scientists, machine learning practitioners, researchers delving into graph-based data, and software engineers crafting graph-related applications, this book offers theoretical and practical guidance with real-world examples. A foundational grasp of ML concepts and Python is presumed.

What you will learn

  • Discover how to extract business value through a graph-centric approach
  • Develop a basic understanding of learning graph attributes using machine learning
  • Identify the limitations of traditional deep learning with graph data and explore specialized graph-based architectures
  • Understand industry applications of graph deep learning, including recommender systems and NLP
  • Identify and overcome challenges in production such as scalability and interpretability
  • Perform node classification and link prediction using PyTorch Geometric
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Table of Contents

18 Chapters
Part 1: Foundations of Graph Learning Chevron down icon Chevron up icon
Part 1: Foundations of Graph Learning
Chapter 1: Introduction to Graph Learning Chevron down icon Chevron up icon
Chapter 1: Introduction to Graph Learning
Do we need graphs?
Formalizing graphs
Types and properties of graphs
Graph data structures
Traditional graph-based solutions
The need for representation learning
GNNs and the need for a separate vertical
Summary
Chapter 2: Graph Learning in the Real World Chevron down icon Chevron up icon
Chapter 2: Graph Learning in the Real World
Node-level learning
Edge-level learning
Graph-level learning
Real-world applications
Summary
Chapter 3: Graph Representation Learning Chevron down icon Chevron up icon
Chapter 3: Graph Representation Learning
Representation learning – what is it?
Graph representation learning
A framework for graph learning
DeepWalk
Node2Vec
Limitations of shallow encodings
Summary
Part 2: Advanced Graph Learning Techniques Chevron down icon Chevron up icon
Part 2: Advanced Graph Learning Techniques
Chapter 4: Deep Learning Models for Graphs Chevron down icon Chevron up icon
Chapter 4: Deep Learning Models for Graphs
Technical requirements
Message passing in graphs
Decoding GNNs
GCNs
GraphSAGE
GATs
Summary
Chapter 5: Graph Deep Learning Challenges Chevron down icon Chevron up icon
Chapter 5: Graph Deep Learning Challenges
Data-related challenges
Model architecture challenges
Computational challenges
Task-specific challenges
Interpretability and explainability
Summary
Chapter 6: Harnessing Large Language Models for Graph Learning Chevron down icon Chevron up icon
Chapter 6: Harnessing Large Language Models for Graph Learning
Understanding LLMs
Textual data in graphs
LLMs for graph learning
Integrating RAG with graph learning
Challenges in integrating LLMs with graph learning
Summary
Part 3: Practical Applications and Implementation Chevron down icon Chevron up icon
Part 3: Practical Applications and Implementation
Chapter 7: Graph Deep Learning in Practice Chevron down icon Chevron up icon
Chapter 7: Graph Deep Learning in Practice
Setting up the environment
Creating the graph dataset
Node classification – predicting student interests
Link prediction – recommending new friendships
Summary
Chapter 8: Graph Deep Learning for Natural Language Processing Chevron down icon Chevron up icon
Chapter 8: Graph Deep Learning for Natural Language Processing
Graph structures in NLP
Graph-based text summarization
Information extraction (IE) using GNNs
Graph-based dialogue systems
Summary
Chapter 9: Building Recommendation Systems Using Graph Deep Learning Chevron down icon Chevron up icon
Chapter 9: Building Recommendation Systems Using Graph Deep Learning
Fundamentals of recommendation systems
Graph structures in recommendation systems
Graph-based recommendation models
Training graph deep learning models
Scalability and optimization
Explainability in graph-based recommendations
The cold start problem
Summary
Chapter 10: Graph Deep Learning for Computer Vision Chevron down icon Chevron up icon
Chapter 10: Graph Deep Learning for Computer Vision
Traditional CV approaches versus graph-based approaches
Graph construction for visual data
GNNs for image classification
Object detection and segmentation using GNNs
Multi-modal learning with GNNs
Limitations and next steps
Summary
Part 4: Future Directions Chevron down icon Chevron up icon
Part 4: Future Directions
Chapter 11: Emerging Applications Chevron down icon Chevron up icon
Chapter 11: Emerging Applications
Biology and healthcare
Social network analysis
Financial services
Cybersecurity
Energy systems
IoT
Legal governance and compliance
Summary
Chapter 12: The Future of Graph Learning Chevron down icon Chevron up icon
Chapter 12: The Future of Graph Learning
Emerging trends and directions
Advanced architectures and techniques
Integration with other AI domains
Potential breakthroughs and long-term vision
Summary
Index Chevron down icon Chevron up icon
Index
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eBook
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  • eBook eBook €8.99
  • Paperback Paperback €41.99
€8.99 €32.99
€41.99
Building LLM Powered Applications
Building LLM Powered Applications
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May 2024 342 pages
Full star icon 4.2 (22)
eBook
eBook
  • eBook eBook €8.99
  • Paperback Paperback €37.99
€8.99 €29.99
€37.99
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About the authors

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Profile icon Lakshya Khandelwal
Lakshya Khandelwal
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Lakshya Khandelwal holds a bachelor's and master's degree from IIT Kanpur in mathematics and computer science and has 8+ years of experience in building scalable machine learning products for multiple tech giants. He has worked as a lead ML engineer with Samsung, building natural language intelligence for the very first version of Bixby. He has also worked as a data scientist with Adobe, developing search bid optimization solutions as part of the advertising cloud suite for major enterprises across the globe. In addition, he has led natural language and forecasting initiatives at Walmart, building next-generation AI products for millions of customers. Lakshya currently leads AI for AirMDR, building agentic AI for the cybersecurity domain.
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Profile icon Subhajoy Das
Subhajoy Das
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Subhajoy Das is a staff data scientist with 7 years of experience under his belt. He graduated from IIT Kharagpur with a bachelor's and master's degree in mathematics and computing. Since then, he has worked in organizations at varying stages of growth: from fast-growing e-commerce start-ups such as Meesho to behemoths such as Adobe. He has driven several pivotal features in every company he has worked in, including building an end-to-end recommendation system for the Meesho app and curating interesting advertising using reinforcement learning-based optimizations in Adobe Advertising. He is currently working at Arista Networks, building AI-driven apps that are responsible for the cybersecurity of several Fortune 500 companies.
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Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

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Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela