At its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs.
Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you’ll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls.
Graph-Powered Machine Learning brief content contents foreword preface acknowledgments about this book Who should read this book? How this book is organized About the code liveBook discussion forum Online resources about the author about the cover illustration Part 1: Introduction Chapter 1: Machine learning and graphs: An introduction 1.1 Machine learning project life cycle 1.1.1 Business understanding 1.1.2 Data understanding 1.1.3 Data preparation 1.1.4 Modeling 1.1.5 Evaluation 1.1.6 Deployment 1.2 Machine learning challenges 1.2.1 The source of truth 1.2.2 Performance 1.2.3 Storing the model 1.2.4 Real time 1.3 Graphs 1.3.1 What is a graph? 1.3.2 Graphs as models of networks 1.4 The role of graphs in machine learning 1.4.1 Data management 1.4.2 Data analysis 1.4.3 Data visualization 1.5 Book mental model Chapter 2: Graph data engineering 2.1 Working with big data 2.1.1 Volume 2.1.2 Velocity 2.1.3 Variety 2.1.4 Veracity 2.2 Graphs in the big data platform 2.2.1 Graphs are valuable for big data 2.2.2 Graphs are valuable for master data management 2.3 Graph databases 2.3.1 Graph database management 2.3.2 Sharding 2.3.3 Replication 2.3.4 Native vs. non-native graph databases 2.3.5 Label property graphs Chapter 3: Graphs in machine learning applications 3.1 Graphs in the machine learning workflow 3.2 Managing data sources 3.2.1 Monitor a subject 3.2.2 Detect a fraud 3.2.3 Identify risks in a supply chain 3.2.4 Recommend items 3.3 Algorithms 3.3.1 Identify risks in a supply chain 3.3.2 Find keywords in a document 3.3.3 Monitor a subject 3.4 Storing and accessing machine learning models 3.4.1 Recommend items 3.4.2 Monitoring a subject 3.5 Visualization 3.6 Leftover: Deep learning and graph neural networks Part 2: Recommendations Chapter 4: Content-based recommendations 4.1 Representing item features 4.2 User modeling 4.3 Providing recommendations 4.4 Advantages of the graph approach Chapter 5: Collaborative filtering 5.1 Collaborative filtering recommendations 5.2 Creating the bipartite graph for the User-Item dataset 5.3 Computing the nearest neighbor network 5.4 Providing recommendations 5.5 Dealing with the cold-start problem 5.6 Advantages of the graph approach Chapter 6: Session-based recommendations 6.1 The session-based approach 6.2 The events chain and the session graph 6.3 Providing recommendations 6.3.1 Item-based k-NN 6.3.2 Session-based k-NN 6.4 Advantages of the graph approach Chapter 7: Context-aware and hybrid recommendations 7.1 The context-based approach 7.1.1 Representing contextual information 7.1.2 Providing recommendations 7.1.3 Advantages of the graph approach 7.2 Hybrid recommendation engines 7.2.1 Multiple models, single graph 7.2.2 Providing recommendations 7.2.3 Advantages of the graph approach Part 3: Fighting fraud Chapter 8: Basic approaches to graph-powered fraud detection 8.1 Fraud prevention and detection 8.2 The role of graphs in fighting fraud 8.3 Warm-up: Basic approaches 8.3.1 Finding the origin point of credit card fraud 8.3.2 Identifying a fraud ring 8.3.3 Advantages of the graph approach Chapter 9: Proximity-based algorithms 9.1 Proximity-based algorithms: An introduction 9.2 Distance-based approach 9.2.1 Storing transactions as a graph 9.2.2 Creating the k-nearest neighbors graph 9.2.3 Identifying fraudulent transactions 9.2.4 Advantages of the graph approach Chapter 10: Social network analysis against fraud 10.1 Social network analysis concepts 10.2 Score-based methods 10.2.1 Neighborhood metrics 10.2.2 Centrality metrics 10.2.3 Collective inference algorithms 10.3 Cluster-based methods 10.4 Advantages of graphs Part 4: Taming text with graphs Chapter 11: Graph-based natural language processing 11.1 A basic approach: Store and access sequence of words 11.1.1 Advantages of the graph approach 11.2 NLP and graphs 11.2.1 Advantages of the graph approach Chapter 12: Knowledge graphs 12.1 Knowledge graphs: Introduction 12.2 Knowledge graph building: Entities 12.3 Knowledge graph building: Relationships 12.4 Semantic networks 12.5 Unsupervised keyword extraction 12.5.1 Keyword co-occurrence graph 12.5.2 Clustering keywords and topic identification 12.6 Advantages of the graph approach appendix A: Machine learning algorithms taxonomy A.1 Supervised vs. unsupervised learning A.2 Batch vs. online learning A.3 Instance-based vs. model-based learning A.4 Active vs. passive learning Reference appendix B: Neo4j B.1 Neo4j introduction B.2 Neo4j installation B.2.1 Neo4j server installation B.2.2 Neo4j Desktop installation B.3 Cypher B.4 Plugin installation B.4.1 APOC installation B.4.2 GDS Library B.5 Cleaning References appendix C: Graphs for processing patterns and workflows C.1 Pregel C.2 Graphs for defining complex processing workflows C.3 Dataflow References appendix D: Representing graphs References index A B C D E F G H I K L M N O P R S T U V W
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