Hands-On Graph Neural Networks Using Python : Practical Techniques and Architectures for Building Powerful Graph and Deep Learning Apps with PyTorch

Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and apps Purchase of the print or Kindle book includes a free PDF eBook Key Features: Implement state-of-the-art graph neural network architectures in Python Create your own graph datasets from tabular data Build powerful traffic forecasting, recommender systems, and anomaly detection applications Book Description: Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery. Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you'll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps. By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more. What You Will Learn: Understand the fundamental concepts of graph neural networks Implement graph neural networks using Python and PyTorch Geometric Classify nodes, graphs, and edges using millions of samples Predict and generate realistic graph topologies Combine heterogeneous sources to improve performance Forecast future events using topological information Apply graph neural networks to solve real-world problems Who this book is for: This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you're new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book. Contents Preface Part 1: Introduction to Graph Learning 1 Getting Started with Graph Learning Why graphs? Why graph learning? Why graph neural networks? Summary Further reading 2 Graph Theory for Graph Neural Networks Technical requirements Introducing graph properties Directed graphs Weighted graphs Connected graphs Types of graphs Discovering graph concepts Fundamental objects Graph measures Adjacency matrix representation Exploring graph algorithms Breadth-first search Depth-first search Summary 3 Creating Node Representations with DeepWalk Technical requirements Introducing Word2Vec CBOW versus skip-gram Creating skip-grams The skip-gram model DeepWalk and random walks Implementing DeepWalk Summary Further reading Part 2: Fundamentals 4 Improving Embeddings with Biased Random Walks in Node2Vec Technical requirements Introducing Node2Vec Defining a neighborhood Introducing biases in random walks Implementing Node2Vec Building a movie RecSys Summary Further reading 5 Including Node Features with Vanilla Neural Networks Technical requirements Introducing graph datasets The Cora dataset The Facebook Page-Page dataset Classifying nodes with vanilla neural networks Classifying nodes with vanilla graph neural networks Summary Further reading 6 Introducing Graph Convolutional Networks Technical requirements Designing the graph convolutional layer Comparing graph convolutional and graph linear layers Predicting web traffic with node regression Summary Further reading 7 Graph Attention Networks Technical requirements Introducing the graph attention layer Linear transformation Activation function Softmax normalization Multi-head attention Improved graph attention layer Implementing the graph attention layer in NumPy Implementing a GAT in PyTorch Geometric Summary Part 3: Advanced Techniques 8 Scaling Up Graph Neural Networks with GraphSAGE Technical requirements Introducing GraphSAGE Neighbor sampling Aggregation Classifying nodes on PubMed Inductive learning on protein-protein interactions Summary Further reading 9 Defining Expressiveness for Graph Classification Technical requirements Defining expressiveness Introducing the GIN Classifying graphs using GIN Graph classification Implementing the GIN Summary Further reading 10 Predicting Links with Graph Neural Networks Technical requirements Predicting links with traditional methods Heuristic techniques Matrix factorization Predicting links with node embeddings Introducing Graph Autoencoders Introducing VGAEs Further reading 14 Explaining Graph Neural Networks Technical requirements Introducing explanation techniques Explaining GNNs with GNNExplainer Introducing GNNExplainer Implementing GNNExplainer Explaining GNNs with Captum Introducing Captum and integrated gradients Implementing integrated gradients Summary Further reading Part 4: Applications 15 Forecasting Traffic Using A3T-GCN Technical requirements Exploring the PeMS-M dataset Processing the dataset Implementing the A3T-GCN architecture Summary Further reading 16 Detecting Anomalies Using Heterogeneous GNNs Technical requirements Exploring the CIDDS-001 dataset Preprocessing the CIDDS-001 dataset Implementing a heterogeneous GNN Summary Further reading 17 Building a Recommender System Using LightGCN Technical requirements Exploring the Book-Crossing dataset Preprocessing the Book-Crossing dataset Implementing the LightGCN architecture Summary Further reading 18 Unlocking the Potential of Graph Neural Networks for Real-World Applications Index Other Books You May Enjoy Hands-On Graph Neural Networks Using Python is a comprehensive guide to building and training graph neural networks for a variety of real-world applications. With clear explanations and plenty of hands-on examples, this book is a valuable resource for anyone looking to learn about and apply graph neural networks to their own projects.