Distributed Agile

Spis treści książki

• Graph Machine Learning
• Contributors
• About the authors
• About the reviewers
• Preface
  • Who this book is for
  • What this book covers
  • To get the most out of this book
  • Download the example code files
  • Download the color images
  • Conventions used
  • Get in touch
  • Reviews
• 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
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