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Applied Deep Learning on Graphs: Leverage graph data for business applications using specialized deep learning architectures
Gain a deep understanding on how to construct enterprise ready solutions using Deep Learning on Graph Data for wide range of domains. Gain perspective on this emerging field from Data, Algorithm and Engineering viewpoints.
Key FeaturesExplore Graph Data in real-world systems and leverage Graph Learning for impactful business resultsDive deep into popular and specialized graph Deep neural architecturesLearn to build scalable and Productionizable Graph Learning solutionsBook DescriptionThis book provides a comprehensive journey into graph neural networks, guiding readers from foundational concepts all the way to advanced techniques and cutting-edge applications. We begin by motivating why graph data structures are ubiquitous in the era of interconnected information, and why we require specialized deep learning approaches, explaining challenges and with existing methods. Next, readers learn about early graph representation techniques like DeepWalk and node2vec which paved the way for modern advances. The core of the book dives deep into popular graph neural architectures – from essential concepts in graph convolutional and attentional networks to sophisticated autoencoder models to leveraging LLMs and technologies like Retrieval augmented generation on Graph data. With strong theoretical grounding established, we then transition to practical implementations, covering critical topics of scalability, interpretability and key application domains like NLP, recommendations, computer vision and more.
By the end of this book, readers master both underlying ideas and hands-on coding skills on real-world use cases and examples along the way. Readers grasp not just how to effectively leverage graph neural networks today but also the promising frontiers to influence where the field may evolve next.
What you will learnDiscover extracting business value through a graph-centric approachDevelop a basic intuition of learning graph attributes using Machine LearningExplore limitations of traditional Deep Learning with graph data and delve into specialized graph-based architecturesLearn how Graph Deep Learning finds applications in industry, including Recommender Systems, NLP, etcGrasp challenges in production such as scalability and interpretabilityWho this book is forFor 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.
Table of ContentsIntroduction to Graph LearningGraph Learning in real WorldGraph Representation LearningDeep Learning Models for GraphsGraph Learning ChallengesLarge Language Models for Graph LearningGraph Deep Learning in PracticeGraph Deep Learning for NLPBuilding Recommendation systems using Graph Deep learningGraph Deep Learning for Computer VisionOther ApplicationsLimitations and Future
Key FeaturesExplore Graph Data in real-world systems and leverage Graph Learning for impactful business resultsDive deep into popular and specialized graph Deep neural architecturesLearn to build scalable and Productionizable Graph Learning solutionsBook DescriptionThis book provides a comprehensive journey into graph neural networks, guiding readers from foundational concepts all the way to advanced techniques and cutting-edge applications. We begin by motivating why graph data structures are ubiquitous in the era of interconnected information, and why we require specialized deep learning approaches, explaining challenges and with existing methods. Next, readers learn about early graph representation techniques like DeepWalk and node2vec which paved the way for modern advances. The core of the book dives deep into popular graph neural architectures – from essential concepts in graph convolutional and attentional networks to sophisticated autoencoder models to leveraging LLMs and technologies like Retrieval augmented generation on Graph data. With strong theoretical grounding established, we then transition to practical implementations, covering critical topics of scalability, interpretability and key application domains like NLP, recommendations, computer vision and more.
By the end of this book, readers master both underlying ideas and hands-on coding skills on real-world use cases and examples along the way. Readers grasp not just how to effectively leverage graph neural networks today but also the promising frontiers to influence where the field may evolve next.
What you will learnDiscover extracting business value through a graph-centric approachDevelop a basic intuition of learning graph attributes using Machine LearningExplore limitations of traditional Deep Learning with graph data and delve into specialized graph-based architecturesLearn how Graph Deep Learning finds applications in industry, including Recommender Systems, NLP, etcGrasp challenges in production such as scalability and interpretabilityWho this book is forFor 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.
Table of ContentsIntroduction to Graph LearningGraph Learning in real WorldGraph Representation LearningDeep Learning Models for GraphsGraph Learning ChallengesLarge Language Models for Graph LearningGraph Deep Learning in PracticeGraph Deep Learning for NLPBuilding Recommendation systems using Graph Deep learningGraph Deep Learning for Computer VisionOther ApplicationsLimitations and Future
430 pages, Kindle Edition
Published December 27, 2024
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Displaying 1 of 1 review
January 13, 2025
I was offered to read this book in exchange for an honest review.
The introduction is well-written and comprehensive. However, I noticed some redundancies—for example, in the bipartite graphs section: the author first states that recommendation systems are built on graphs, then later mentions that examples of bipartite graphs include the marriage problem and recommendation systems. This felt repetitive.
By the end of the introduction, I found it a bit prolonged. Perhaps the section on graph representation learning could have been split into a separate chapter to improve the flow.
That said, I thoroughly enjoyed the section on representation learning. It explained various approaches clearly, provided illustrative code examples, and discussed their advantages and shortcomings effectively.
The chapters on deep learning approaches were solid, with the discussion of LLM applications being especially engaging, given the current popularity of the topic. The chapter addressing the challenges of deep learning on graphs stood out as particularly well-crafted and insightful.
I recommend this book to anyone looking to familiarize themselves with how graphs are used in modern machine learning and explore deep learning applications in this domain.
The introduction is well-written and comprehensive. However, I noticed some redundancies—for example, in the bipartite graphs section: the author first states that recommendation systems are built on graphs, then later mentions that examples of bipartite graphs include the marriage problem and recommendation systems. This felt repetitive.
By the end of the introduction, I found it a bit prolonged. Perhaps the section on graph representation learning could have been split into a separate chapter to improve the flow.
That said, I thoroughly enjoyed the section on representation learning. It explained various approaches clearly, provided illustrative code examples, and discussed their advantages and shortcomings effectively.
The chapters on deep learning approaches were solid, with the discussion of LLM applications being especially engaging, given the current popularity of the topic. The chapter addressing the challenges of deep learning on graphs stood out as particularly well-crafted and insightful.
I recommend this book to anyone looking to familiarize themselves with how graphs are used in modern machine learning and explore deep learning applications in this domain.
Displaying 1 of 1 review