Tackle the core challenges related to enterprise-ready graph representation and learning. With this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. You'll explore core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining.

Drawing on their experience building scalable and production-ready graph learning pipelines, the authors take you through the process of building robust graph learning systems in a world of dynamic and evolving graphs.

• Understand the importance of graph learning for boosting enterprise-grade applications
• Navigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelines
• Use traditional and advanced graph learning techniques to tackle graph use cases
• Use and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applications
• Design and implement a graph learning algorithm using publicly available and syntactic data
• Apply privacy-preserving techniques to the graph learning process

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Spis treści

Scaling Graph Learning for the Enterprise. Production-Ready Graph Learning and Inference eBook -- spis treści

• Preface
  • What Is Graph Learning for the Enterprise?
  • Why Does Graph Learning Matter for the Enterprise?
  • Who Is This Book For?
  • Overview of the Chapters
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgements
• 1. Introduction to Graphs
  • The Power of Enterprise Graph Learning and Inference at Scale
  • A Birds-Eye View: Navigating the Books Chapters
  • Graphs and Graph Learning
    • What Is a Graph?
    • Graph Data Representation
    • Graph Learning
    • Scalable Graph Learning: Addressing the Requirements
    • Advantages of Scalable Graph Learning in Enterprise
  • Large-Scale Graphs in Real-World Enterprises: Use Cases
    • Travel-Time Predictions on Google Maps
    • Drug Development: Halicin
    • Fraud Detection
  • The Evolution of Graphs and Graph Learning: From Early Beginnings to Modern Applications
    • Era 1: The Foundation of Graph Theory and Algorithms (1736-1970)
    • Era 2: More Advancement in Graph Algorithms and Technologies (1970-1999)
    • Era 3: Emergence of Graph Databases and Graph Query Languages (2000-2006)
    • Era 4: Graph Analytics and Traditional Machine Learning (2007-2011)
    • Era 5: Rise of Graph Neural Networks (2012-2018)
    • Era 6: Scalability, Robustness, and Enterprise Applications (2019-Present)
    • Challenges of Enterprise-Ready Graph Learning Systems
    • Data Harmonization Challenges
    • Computationally Intensive Workloads
    • Dynamic Evolving Graphs
    • Active Monitoring and Drift Detection
    • Real-Time Inference
  • Summary
• 2. The Graph Machine Learning Pipeline
  • The Graph Data Pipeline
    • Definition of Graph Data and Graph Data Levels
      • Natural graph data
      • Graphically represented phenomena
      • Structured data
      • Unstructured data
    • Graph Data Sourcing and Understanding
      • Graph data sourcing
      • Understanding visual graph data
    • Graph Data Preparation
      • Graph data ingestion
      • Graph data storage
      • Graph data preprocessing
      • Why enrich graph data with external data?
      • Graph data harmonization
  • The Graph Training and Inference Pipelines
    • GML Training Pipeline Overview
      • Graph feature engineering
      • Graph model building cycle
    • GML Inference Pipeline Overview
      • GML model registration
      • GML model serving
      • Monitoring and feedback loop
  • Summary
• 3. Traditional Machine Learning for Graphs
  • Approaches to Graph Machine Learning
    • Traditional Graph-Based Machine Learning
    • Nontraditional Graph-Based Machine Learning
  • Representing Graphs for Traditional ML
    • Graph Representation
    • Representing Amazon Copurchasing Networks as Graphs
    • Navigating Graph Tasks in the Amazon Copurchasing Dataset
  • Graph Feature Engineering
    • Importance and Challenges
    • Types of Graph Features
      • Node-level features
      • Edge-level features
      • Graph-level features
    • Hands-on: Extracting Features for the Amazon Copurchasing Graph
      • Graph feature derivation
      • Leveraging graph-derived metrics to enrich product attributes
  • Graph Features in ML Modeling
    • Task and Techniques Overview
      • Node classification
      • Link prediction
      • Graph clustering
  • Predicting High-Rated Products with a Prediction Model
  • Feature Learning with Node Embeddings
    • Random Walk Algorithm
    • Amazon Copurchasing Dataset and Node Embeddings
      • Node embeddings use case for Amazon copurchasing dataset
      • Generating node embeddings
  • Summary
• 4. PyGraf: End-to-End Graph Learning and Serving
  • Graph Libraries Overview
    • Challenges of Open Source Graph Libraries: PyGraf Opportunities
    • PyGraf: A Solution for Streamlined Graph Learning and Serving
  • Introduction to PyGraf
    • PyGraf Key Features
    • PyGraf Purposes: Empowering Dynamic Environments
  • Architecture and Core Capabilities
    • Core Components Layer
    • Adaptation and Integration Layer
    • Best Practices Layer
  • In-Depth Exploration of Core Library Components
    • Data Component
      • Pre-integrated datasets
      • Extending with new datasets
      • Connectors in PyGraf
      • Preprocessors and Transformation in PyGraf
    • Training Component
      • Graph learning (Training)
      • Fine-tuning
      • Model selection and validation
    • Serving Component
      • Serving mechanisms: Scaling and optimizing for production
      • NVIDIA Triton Inference Server example
    • Privacy Preserving Component
      • Federated learning: Principles and implementation in PyGraf
      • Implementing federated learning in PyGraf
  • End-to-End Example Using PyGraf: Amazon Copurchasing Dataset
    • Preprocessing and Transformation
    • Model Training
    • Evaluation and Model Selection
    • Deployment and Monitoring
  • Summary
• 5. Graph Neural Networks
  • Introduction to Graph Neural Networks
    • The Significance of GNNs in Graph Learning
    • Overview of GNN Applications
    • Foundations of GNNs
      • Message passing in GNNs
      • Learning process
    • Graph Convolutional Networks
      • The convolution operation
      • The GCN layer operation
      • Simple example
    • Transition from Traditional Graph Learning to GCNs
    • How GCNs Simplify Feature Engineering
  • Hands-on E2E Example Using PyG and the PyGraf Interface
    • PyG: Karate Club Example
      • PyG installation
      • PyG graph data representation
      • PyG GCN API interface
      • Embedding the Karate Club network
    • Cora Node Classification
      • Data preparation
      • GCN model architecture
      • Embedding the Cora network
      • Model training and evaluation
      • Model testing
    • PyGraf API Interface
      • Initializing modules and dependencies
      • Data loading and preprocessing
      • Model initialization
      • Setting up the training components
      • Training logic
      • Model evaluation
      • Visualization of embeddings
  • Limitations of GCN-Based Architecture
  • Summary
• 6. Advanced Techniques in Graph Learning
  • Different Types of Graphs
    • Homogeneous Graphs
    • Heterogeneous Graphs
    • Temporal Graphs
  • Graph Embedding Models
    • How Do Knowledge Graph Embeddings Work?
    • Training Knowledge Graph Embedding Models
    • Embedding Interaction Methods
      • Distance-based embedding interactions
      • Factorization-based embedding interactions
    • Training Objectives
    • Strengths of Graph Embedding Models
    • Example: Learning on Freebase Dataset
      • Loading and exploring data
      • Initializing the model and training configuration
      • Defining and executing the training loop
  • Attention on Graphs
    • Feature Vectors
    • Attention Mechanism
    • Multihead Attention
    • Example: Citation Network
      • Feature combination
      • Score calculation
      • Normalization (softmax)
      • Weighted sum
      • Update paper i information
  • CiteSeer Use Case Example
    • Data Preprocessing and Visualization
    • Model Training Using PyG
    • Model Testing
    • Embeddings Visualization
  • Summary
• 7. Scalable Graph Neural Networks
  • Challenges in Scaling Graph Learning Models
    • Computational Challenges
    • Memory Constraints
    • Data Complexity and Size
  • Mini-Batching in Graph Neural Networks
    • Definition and Importance of Mini-Batching
    • Techniques for Effective Mini-Batching
      • Implementation with PyG DataLoader
      • Example: Batching pair and bipartite graphs
  • Memory-Efficient Training Techniques
    • Gradient Checkpointing
    • Subgraph Sampling Methods
    • Layer-Wise Relevance Propagation
  • Distributed Data and Compute Strategies
    • Distributed Execution Strategies
      • Conventional mini-batching
      • Factored mini-batching
      • Operator-parallel mini-batching
      • Pull-push parallelism
      • Comparing the approaches
    • Graph Partitioning Strategies
      • Node splitting
      • Edge splitting
    • Distributed Graph Learning Tools
      • PyTorch Geometric
      • PaddlePaddle: Paddle Graph Learning (PGL)
  • Distributed Training with PyG
    • Prepare and Partition the Graph Data
    • Manual Execution of Distributed Training
    • Key Components of Distributed Training with PyG
  • Advanced Architectures for Scaling Graph Neural Networks
    • Sparsity Exploitation
    • Approximation Techniques
    • Use of External Memory
  • Summary
• 8. Enterprise Applications of Graphs
  • Customer and Market Insights
    • Customer Segmentation
    • Social Network Analysis
    • Recommendation Engines
  • Operations and Supply Chain Management
    • Network Optimization in Supply Chains
    • Inventory Management
    • Risk Management and Supply Chain Resilience
      • Supplier selection and relationship management
      • Demand forecasting and distribution planning
      • Transportation and logistics optimization
    • Real-World Applications
      • Amazon: Supply chain and logistics optimization
      • Walmart: Inventory management and supplier relationships
      • UPS: Transportation and logistics
  • Security and Risk Management
    • Threat Detection and Analysis
    • Identity and Access Management
    • Fraud Detection
    • Cybersecurity Incident Response
    • Risk Management and Compliance
  • Healthcare and Life Sciences
    • Patient Journey Mapping and Personalization
    • Drug Discovery and Development
      • Accelerating drug discovery and repurposing
      • Optimizing drug safety monitoring
    • Clinical Trial Optimization
    • Genomics and Personalized Medicine
    • Popular Life Science Products
      • BenevolentAI drug repurposing
      • Tempus genomic analysis
      • Medidata Rave for clinical trials
  • Summary
• 9. Privacy-Preserving Graph Learning
  • The Importance of Privacy in Graph Learning
  • Enterprise Examples and Applications
  • Overview of Privacy-Preserving Techniques
    • Privacy Threats in Graph Learning
      • Types of privacy attacks
      • Adversarial models and background knowledge
    • Case Studies of Privacy Breaches
      • The Netflix Prize data leak
      • Social network de-anonymization
  • Privacy-Preserving Techniques for Graph Data
    • Graph Data Anonymization
    • k-Anonymity and t-Closeness in Graphs
    • Graph Modification Techniques
      • Edge perturbation
      • Node aggregation
      • Utility considerations
    • Edge Differential Privacy
    • Node Differential Privacy
    • Synthetic Graph Data Generation
      • Graph GANs
      • Variational autoencoders (VAEs)
  • Privacy-Preserving Graph Computation
    • SMPC for Graphs
      • Concept of SMPC
      • SMPC Protocols for Graph Computation
    • Homomorphic Encryption in Graph Learning
      • Concept of homomorphic encryption
      • Applying homomorphic encryption to graph learning
    • Differentially Private Learning Algorithms
      • Concept of differential privacy in learning
      • Techniques for differentially private learning
      • Applying differential privacy to graph models
      • Example: Training a GNN with differential privacy
      • Training with differential privacy considerations
  • Federated Graph Learning
    • Principles and Architecture
      • Federated learning architecture
      • Training process
      • Relevance to graph data
      • Motivation for federated graph learning
    • Federated Graph Neural Networks
      • Model architectures
      • Fintech example
    • Training and Communication Protocols
      • Federated training protocol for GNNs
      • Communication protocols
    • Privacy Enhancements in Federated Graph Learning
      • Secure aggregation protocols
      • Example of a secure aggregation protocol
      • Differential privacy in federated settings
  • Applications of Privacy-Preserving Graph Learning
    • Privacy-Preserving Techniques in Social Network Analysis
      • Anonymization of social graphs
      • Differential privacy in social networks
      • Federated social network analysis
    • Use Cases
  • Case Study: Applying FedGraphNN to a Recommender System Using Epinions Data
    • Dataset
    • Importance of Applying Federated Learning
    • Data Processing
    • Federated Training Logic
      • Model definition: GCN for link prediction
      • Trainer definition: Federated training logic
    • Running the Federated Pipeline
  • Summary
• 10. Graph Inference and Deployment Strategies
  • Deployment Strategies
    • Canary Deployments: Testing the Waters
    • BlueGreen Deployments: Smooth Transitions
    • Shadow Deployments: Flying Under the Radar
    • Edge Deployments: Taking It to the Edge
    • A/B Testing Deployments: Controlled Experiments
    • Progressive Rollouts: Scaling Up Gradually
    • Containerized Deployments
    • Hardware Considerations
      • CPU inference
      • GPU inference
      • Emerging hardware technologies
      • Balancing hardware choices
  • Inference Runtimes
    • Inference Frameworks and Libraries
    • Precomputations and Caching
      • Precomputed embeddings
      • Caching inference results
      • Trade-offs in precomputation and caching
    • Online Versus Offline Inference
      • Online inference: Real-time predictions
      • Offline inference: Batch processing
      • Balancing online and offline inference
  • Model Optimization Techniques
    • Quantization
      • How quantization works
      • Types of quantization
      • Technical considerations
    • Pruning
      • How pruning works
      • Advantages of pruning for graph models
      • Limitations and challenges of pruning
    • Knowledge Distillation
      • How knowledge distillation works
      • Advantages of knowledge distillation for graph model inference
      • Teacherstudent architectures for graph models
  • Scaling Inference for Large Graphs
    • Distributed Inference Systems
    • Graph Partitioning for Scalable Inference
      • How graph partitioning works
      • Techniques for graph partitioning
      • Challenges and trade-offs in graph partitioning
    • Incremental Inference
      • The fundamentals of incremental inference
      • Techniques in incremental inference
      • Challenges in incremental inference
      • Addressing challenges in incremental inference
  • Summary
• 11. Monitoring and Feedback Loops
  • Challenges in Monitoring Graph Models
    • Graph-Specific Metrics
    • Dynamic Graphs
    • Scale and Complexity
    • System and Deployment Issues
  • Designing a Monitoring Framework
    • Dealing with Ever-Changing Connections (Graph Topology)
    • Checking the Meaning (Evaluating Embeddings)
    • The Balancing Act (System Performance Versus Graph Tasks)
    • Data Collection Mechanisms
      • Real-time monitoring: Catching things as they happen
      • Batch monitoring: Scheduled check-ups
    • Monitoring Deployed Graph Models
      • Performance monitoring: Is the model achieving its goal?
      • Structural monitoring: Is the graph itself changing?
      • Embedding monitoring: Are learned representations still meaningful?
    • Thresholds for Alerts
    • Visualization Tools
      • Real-time dashboards
      • Historical trends
      • Custom alerts
  • Feedback Loops in Graph Systems
    • User Feedback: Learning from Interaction
    • System Feedback: Optimizing Model Performance
    • Data Feedback: Adapting to a Changing World
    • Synergy Between Feedback Loops
    • Closed-Loop Systems: Automating Adaptation
      • What is a closed-loop system?
      • Key components of closed-loop graph systems
    • Adaptive Retraining Pipelines
      • Monitoring model health
      • Auto retraining triggering
      • Anomaly detection
    • Notification Systems
      • Alert design principles
      • Integration strategies
  • Summary
• 12. Future Trends: Graph Learning and LLMs
  • Introduction to Graph-Enhanced LLMs
    • LLMs and Their Transformative Impact
    • The Need for External Memory: The RAG Setup
    • Benefits of Graph-Enhanced RAG Setup
  • Retrieval Augmentation with Graph Integration
    • Challenges in Traditional Retrieval-Augmented Generation
    • Graph Integration
  • GraphRAG Methodology and Pipeline
    • Indexing Process
      • Text segmentation
      • Entity, relationship, and claim extraction
      • Knowledge graph construction
      • Hierarchical clustering and community formation
      • Embedding generation for graph nodes
      • Finalizing the index
    • Querying Process
      • Query parsing and understanding
      • Global search via community summaries
      • Localized entity search and graph traversal
      • Contextual augmentation for the LLM
      • Answer generation with evidence provenance
    • Enhanced Capabilities and Whole-Dataset Reasoning
    • Implementation Considerations
    • Baseline RAG Versus GraphRAG
  • RAG with Knowledge Graphs for Customer Service Question Answering
  • Summary
• Index