This 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.

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Spis treści

Applied Deep Learning on Graphs. Leverage graph data for business applications using specialized deep learning architectures eBook -- spis treści

• 1. Introduction to Graph Learning
• 2. Graph Learning in the Real World
• 3. Graph Representation Learning
• 4. Deep Learning Models for Graphs
• 5. Graph Deep Learning Challenges
• 6. Harnessing Large Language Models for Graph Learning
• 7. Graph Deep Learning in Practice
• 8. Graph Deep Learning for Natural Language Processing
• 9. Building Recommendation Systems Using Graph Deep Learning
• 10. Graph Deep Learning for Computer Vision
• 11. Emerging Applications
• 12. The Future of Graph Learning