THE WITCH HAREM OF CAULDRON STREET: THE RETURN OF HARBINGER

Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods to solve real-life problems Book Description Graph Machine Learning provides a new set of tools for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. You will start with a brief introduction to graph theory and graph machine learning, understanding their potential. As you proceed, you will become well versed with the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll then build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. Moving ahead, you will cover real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. Finally, you will learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, before progressing to explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications. What you will learn Write Python scripts to extract features from graphs Distinguish between the main graph representation learning techniques Become well-versed with extracting data from social networks, financial transaction systems, and more Implement the main unsupervised and supervised graph embedding techniques Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more Deploy and scale out your application seamlessly Who this book is for This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. The book will also be useful for machine learning developers or anyone who want to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required. Intermediate-level working knowledge of Python programming and machine learning is also expected to make the most out of this book. Table of Contents Getting Started with Graphs Graph Machine Learning Unsupervised Graph Learning Supervised Graph Learning Problems with Machine Learning on Graphs Social Network Graphs Text Analytics and Natural Language Processing Using Graphs Graph Analysis for Credit Card Transactions Building a Data-Driven Graph-Powered Application Novel Trends on Graphs Cover Title Page Copyright and Credits Dedication Contributors Table of Contents Preface Section 1 – Introduction to Graph Machine Learning Chapter 1: Getting Started with Graphs Technical requirements Introduction to graphs with networkx Types of graphs Graph representations Plotting graphs networkx Gephi Graph properties Integration metrics Segregation metrics Centrality metrics Resilience metrics Benchmarks and repositories Examples of simple graphs Generative graph models Benchmarks Dealing with large graphs Summary Chapter 2: Graph Machine Learning Technical requirements Understanding machine learning on graphs Basic principles of machine learning The benefit of machine learning on graphs The generalized graph embedding problem The taxonomy of graph embedding machine learning algorithms The categorization of embedding algorithms Summary Section 2 – Machine Learning on Graphs Chapter 3: Unsupervised Graph Learning Technical requirements The unsupervised graph embedding roadmap Shallow embedding methods Matrix factorization Skip-gram Autoencoders TensorFlow and Keras – a powerful combination Our first autoencoder Denoising autoencoders Graph autoencoders Graph neural networks Variants of GNNs Spectral graph convolution Spatial graph convolution Graph convolution in practice Summary Chapter 4: Supervised Graph Learning Technical requirements The supervised graph embedding roadmap Feature-based methods Shallow embedding methods Label propagation algorithm Label spreading algorithm Graph regularization methods Manifold regularization and semi-supervised embedding Neural Graph Learning Planetoid Graph CNNs Graph classification using GCNs Node classification using GraphSAGE Summary Chapter 5: Problems with Machine Learning on Graphs Technical requirements Predicting missing links in a graph Similarity-based methods Embedding-based methods Detecting meaningful structures such as communities Embedding-based community detection Spectral methods and matrix factorization Probability models Cost function minimization Detecting graph similarities and graph matching Graph embedding-based methods Graph kernel-based methods GNN-based methods Applications Summary Section 3 – Advanced Applications of Graph Machine Learning Chapter 6: Social Network Graphs Technical requirements Overview of the dataset Dataset download Loading the dataset using networkx Network topology and community detection Topology overview Node centrality Community detection Embedding for supervised and unsupervised tasks Task preparation node2vec-based link prediction GraphSAGE-based link prediction Hand-crafted features for link prediction Summary of results Summary Chapter 7: Text Analytics and Natural Language Processing Using Graphs Technical requirements Providing a quick overview of a dataset Understanding the main concepts and tools used in NLP Creating graphs from a corpus of documents Knowledge graphs Bipartite document/entity graphs Building a document topic classifier Shallow learning methods Graph neural networks Summary Chapter 8: Graph Analysis for Credit Card Transactions Technical requirements Overview of the dataset Loading the dataset and graph building using networkx Network topology and community detection Network topology Community detection Embedding for supervised and unsupervised fraud detection Supervised approach to fraudulent transaction identification Unsupervised approach to fraudulent transaction identification Summary Chapter 9: Building a Data-Driven Graph-Powered Application Technical requirements Overview of Lambda architectures Lambda architectures for graph-powered applications Graph processing engines Graph querying layer Selecting between Neo4j and GraphX Summary Chapter 10: Novel Trends on Graphs Technical requirements Learning about data augmentation for graphs Sampling strategies Exploring data augmentation techniques Learning about topological data analysis Topological machine learning Applying graph theory in new domains Graph machine learning and neuroscience Graph theory and chemistry and biology Graph machine learning and computer vision Recommendation systems Summary Why subscribe? About PACKT Other Books You May Enjoy Index **Build machine learning algorithms using graph data and efficiently exploit topological information within your models** * Implement machine learning techniques and algorithms in graph data * Identify the relationship between nodes in order to make better business decisions * Apply graph-based machine learning methods to solve real-life problems Graph Machine Learning provides a new set of tools for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. You will start with a brief introduction to graph theory and graph machine learning, understanding their potential. As you proceed, you will become well versed with the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll then build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. Moving ahead, you will cover real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. Finally, you will learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, before progressing to explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications. * Write Python scripts to extract features from graphs * Distinguish between the main graph representation learning techniques * Become well-versed with extracting data from social networks, financial transaction systems, and more * Implement the main unsupervised and supervised graph embedding techniques * Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more * Deploy and scale out your application seamlessly This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. The book will also be useful for machine learning developers or anyone who want to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required. Intermediate-level working knowledge of Python programming and machine learning is also expected to make the most out of this book. 1. Getting Started with Graphs 2. Graph Machine Learning 3. Unsupervised Graph Learning 4. Supervised Graph Learning 5. Problems with Machine Learning on Graphs 6. Social Network Graphs 7. Text Analytics and Natural Language Processing Using Graphs 8. Graph Analysis for Credit Card Transactions 9. Building a Data-Driven Graph-Powered Application 10. Novel Trends on Graphs Build Machine Learning Algorithms Using Graph Data And Efficiently Exploit Topological Information Within Your Models Key Features: Implement Machine Learning Techniques And Algorithms In Graph Data Identify The Relationship Between Nodes In Order To Make Better Business Decisions Apply Graph-based Machine Learning Methods To Solve Real-life Problems Book Description: Graph Machine Learning Provides A New Set Of Tools For Processing Network Data And Leveraging The Power Of The Relation Between Entities That Can Be Used For Predictive, Modeling, And Analytics Tasks. You Will Start With A Brief Introduction To Graph Theory And Graph Machine Learning, Understanding Their Potential. As You Proceed, You Will Become Well Versed With The Main Machine Learning Models For Graph Representation Learning: Their Purpose, How They Work, And How They Can Be Implemented In A Wide Range Of Supervised And Unsupervised Learning Applications. You'll Then Build A Complete Machine Learning Pipeline, Including Data Processing, Model Training, And Prediction In Order To Exploit The Full Potential Of Graph Data. Moving Ahead, You Will Cover Real-world Scenarios Such As Extracting Data From Social Networks, Text Analytics, And Natural Language Processing (nlp) Using Graphs And Financial Transaction Systems On Graphs. Finally, You Will Learn How To Build And Scale Out Data-driven Applications For Graph Analytics To Store, Query, And Process Network Information, Before Progressing To Explore The Latest Trends On Graphs. By The End Of This Machine Learning Book, You Will Have Learned Essential Concepts Of Graph Theory And All The Algorithms And Techniques Used To Build Successful Machine Learning Applications. What You Will Learn: Write Python Scripts To Extract Features From Graphs Distinguish Between The Main Graph Representation Learning Techniques Become Well-versed With Extracting Data From Social Networks, Financial Transaction Systems, And More Implement The Main Unsupervised And Supervised Graph Embedding Techniques Get To Grips With Shallow Embedding Methods, Graph Neural Networks, Graph Regularization Methods, And More Deploy And Scale Out Your Application Seamlessly Who This Book Is For: This Book Is For Data Analysts, Graph Developers, Graph Analysts, And Graph Professionals Who Want To Leverage The Information Embedded In The Connections And Relations Between Data Points To Boost Their Analysis And Model Performance. The Book Will Also Be Useful For Data Scientists And Machine Learning Developers Who Want To Build Ml-driven Graph Databases. A Beginner-level Understanding Of Graph Databases And Graph Data Is Required. Intermediate-level Working Knowledge Of Python Programming And Machine Learning Is Also Expected To Make The Most Out Of This Book.