What this book covers
Chapter 1, Introduction to Graph Learning, introduces graph learning, explaining how graphs can represent complex relationships more efficiently than traditional tabular data structures, demonstrated through various types of graphs, their properties, and computational representations.
Chapter 2, Graph Learning in the Real World, provides a comprehensive exploration of graph learning across three fundamental levels: the node level (predicting attributes of individual nodes), the edge level (analyzing relationships between nodes), and the graph level (studying entire graph structures), with practical applications in recommendation systems, knowledge graphs, cybersecurity, and natural language processing (NLP).
Chapter 3, Graph Representation Learning, explains graph representation learning techniques, focusing on shallow encoding methods such as DeepWalk and Node2Vec, which use random walks to generate node embeddings in graphs.
Chapter 4, Deep Learning Models for Graphs, provides a detailed exploration of graph neural networks (GNNs), covering message-passing mechanisms and three key architectures: graph convolutional networks (GCNs), GraphSAGE, and graph attention networks (GATs).
Chapter 5, Graph Deep Learning Challenges, exhaustively covers the major challenges in graph deep learning, including data-related issues, model architecture limitations, computational constraints, task-specific problems, and interpretability concerns in GNNs.
Chapter 6, Harnessing Large Language Models for Graph Learning, explores how large language models (LLMs) can enhance graph learning tasks through feature enhancement, prediction capabilities, and retrieval-augmented generation (RAG) approaches.
Chapter 7, Graph Deep Learning in Practice, demonstrates practical implementations of graph deep learning techniques for social network analysis using PyTorch Geometric, focusing on node classification and link prediction tasks in a university student network.
Chapter 8, Graph Deep Learning for Natural Language Processing, explores how graph deep learning techniques are applied to NLP tasks, covering linguistic graph structures, text summarization, information extraction, and dialogue systems.
Chapter 9, Building Recommendation Systems Using Graph Deep Learning, comprehensively covers how to build and implement recommendation systems using graph deep learning, including fundamental concepts, graph structures, model architectures, and training techniques.
Chapter 10, Graph Deep Learning for Computer Vision, covers how GNNs can enhance computer vision tasks by representing visual data as graphs, enabling better modeling of relationships and structural properties compared to traditional grid-based convolutional neural network (CNN) approaches.
Chapter 11, Emerging Applications, explores the latest practical applications of graph deep learning across six major domains: biology/healthcare, social networks, financial services, cybersecurity, energy systems, and Internet of Things (IoT).
Chapter 12, The Future of Graph Learning, explores the future trajectory of graph learning, covering emerging trends, advanced architectures, artificial intelligence (AI) integration, and potential breakthroughs in areas such as quantum computing, artificial general intelligence (AGI), and metaverse applications.
