Upgrade your machine learning models with graph-based algorithms, the perfect …
Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data.
In Graph-Powered Machine Learning you will learn:Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro’s extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients!
Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems.
Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks.
For readers comfortable with machine learning basics.
Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science.
The single best source of information for graph-based machine learning.Part 1 Introduction
Chapter 1 Machine learning and graphs: An introduction
Chapter 1 Business understanding
Chapter 1 Machine learning challenges
Chapter 1 Performance
Chapter 1 Graphs
Chapter 1 Graphs as models of networks
Chapter 1 The role of graphs in machine learning
Chapter 2 Graph data engineering
Chapter 2 Velocity
Chapter 2 Graphs in the big data platform
Chapter 2 Graphs are valuable for big data
Chapter 2 Graphs are valuable for master data management
Chapter 2 Graph databases
Chapter 2 Sharding
Chapter 2 Native vs. non-native graph databases
Chapter 2 Label property graphs
Chapter 3 Graphs in machine learning applications
Chapter 3 Managing data sources
Chapter 3 Detect a fraud
Chapter 3 Recommend items
Chapter 3 Algorithms
Chapter 3 Find keywords in a document
Chapter 3 Storing and accessing machine learning models
Chapter 3 Monitoring a subject
Chapter 3 Visualization
Chapter 3 Leftover: Deep learning and graph neural networks
Part 2 Recommendations
Chapter 4 Content-based recommendations
Chapter 4 Representing item features
Chapter 4 Representing item features
Chapter 4 User modeling
Chapter 4 Providing recommendations
Chapter 4 Providing recommendations
Chapter 4 Providing recommendations
Chapter 5 Collaborative filtering
Chapter 5 Collaborative filtering recommendations
Chapter 5 Computing the nearest neighbor network
Chapter 5 Computing the nearest neighbor network
Chapter 5 Providing recommendations
Chapter 5 Dealing with the cold-start problem
Chapter 6 Session-based recommendations
Chapter 6 The events chain and the session graph
Chapter 6 Providing recommendations
Chapter 6 Session-based k-NN
Chapter 7 Context-aware and hybrid recommendations
Chapter 7 Representing contextual information
Chapter 7 Providing recommendations
Chapter 7 Providing recommendations
Chapter 7 Advantages of the graph approach
Chapter 7 Providing recommendations
Part 3 Fighting fraud
Chapter 8 Basic approaches to graph-powered fraud detection
Chapter 8 Fraud prevention and detection
Chapter 8 The role of graphs in fighting fraud
Chapter 8 Warm-up: Basic approaches
Chapter 8 Identifying a fraud ring
Chapter 9 Proximity-based algorithms
Chapter 9 Distance-based approach
Chapter 9 Creating the k-nearest neighbors graph
Chapter 9 Identifying fraudulent transactions
Chapter 9 Identifying fraudulent transactions
Chapter 10 Social network analysis against fraud
Chapter 10 Social network analysis concepts
Chapter 10 Score-based methods
Chapter 10 Neighborhood metrics
Chapter 10 Centrality metrics
Chapter 10 Collective inference algorithms
Chapter 10 Cluster-based methods
Part 4 Taming text with graphs
Chapter 11 Graph-based natural language processing
Chapter 11 A basic approach: Store and access sequence of words
Chapter 11 NLP and graphs
Chapter 11 NLP and graphs
Chapter 12 Knowledge graphs
Chapter 12 Knowledge graph building: Entities
Chapter 12 Knowledge graph building: Relationships
Chapter 12 Semantic networks
Chapter 12 Unsupervised keyword extraction
Chapter 12 Unsupervised keyword extraction
Chapter 12 Keyword co-occurrence graph
Appendix A. Machine learning algorithms taxonomy
Appendix C Graphs for processing patterns and workflows
Appendix C Graphs for defining complex processing workflows
Appendix D. Representing graphs