Graph-Powered Machine Learning
Alessandro Negro
  • MEAP began October 2018
  • Publication in December 2020 (estimated)
  • ISBN 9781617295645
  • 503 pages (estimated)
  • printed in black & white

A wonderful introduction to graphs for machine learning enthusiasts, as well as a great entrée into machine learning for graph experts.

Erik Sapper
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 teaches you how to use graph-based algorithms and data organization strategies to develop superior machine learning applications.

About the Technology

Graph-based machine learning is an incredibly powerful tool for any task that involves pattern matching in large data sets. Applications include security concerns like identifying fraud or detecting network intrusions, application areas like social networking or natural language processing, and better user experiences through accurate recommendations and smart search. By organizing and analyzing your data as graphs your applications work more fluidly with graph-centric algorithms like nearest neighbor or page rank where it’s important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon Neptune, are readily available tools that support graph-powered machine learning.

About the book

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. Author Alessandro Negro’s extensive experience building graph-based machine learning systems shines through in every chapter, as you learn from examples and concrete scenarios based on his own work with real clients!
Table of Contents detailed table of contents

Part 1: Introduction

1 Machine Learning and Graph: An introduction

1.1 Introduction to Machine Learning

1.1.1 Machine Learning Project Lifecycle

1.1.2 Algorithm taxonomies

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 Representing Graphs

1.3.3 Graph as model of networks

1.3.4 Property graph model

1.4 The role of graph in the machine learning

1.4.1 Data Management

1.4.2 Data Analysis

1.4.3 Data Visualization

1.5 Summary

1.6 References

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 Big Data Platform

2.2.1 Graphs are valuable for big data

2.2.2 Graph for master data management

2.3 Graph Databases

2.3.1 Graph Data Management

2.3.2 Sharding

2.3.3 Replication

2.3.4 Native graph vs not native graph

2.3.5 Neo4j

2.4 Summary

2.5 References

3 Graphs in Machine Learning Application

3.1 Graphs in Machine Learning Workflow

3.1.1 Managing Data Sources

3.1.2 Algorithms

3.1.3 Storing and Accessing Machine Learning Model

3.1.4 Visualization

3.2 Graph as processing pattern

3.2.1 Pregel

3.3 Graph for defining complex processing workflow

3.3.1 DataFlow

3.4 Summary

3.5 References

Part 2: Graph Data Modelling and Algorithms

4 Content-Based Recommendation

4.1 Recommendation engines—​An introduction

4.2 Content-based recommendations

4.2.1 Representing item features

4.2.2 User modeling

4.2.3 Providing recommendations

4.2.4 Advantages of the graph approach

4.3 Summary

4.4 References

5 Collaborative Filtering

5.1 Collaborative filtering recommendations

5.1.1 Creating the bipartite graph for the user-item dataset

5.1.2 Computing the nearest neighbor network

5.1.3 Providing recommendations

5.1.4 Advantages of the graph approach

5.2 Summary

5.3 References

6 Session-Based Recommendation

6.1 The session-based approach

6.2 The events chain and the session graph

6.2.1 Data Ingestion

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

6.5 Summary

6.6 References

7 Context-Aware and Hybrid Recommendation

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, a single graph

7.2.2 Providing recommendations

7.2.3 Advantages of the graph approach

7.3 Summary

7.4 References

8 Fighting frauds: Introduction and basic tool

8.1 Fraud prevention and detection

8.2 The role of graphs in fighting fraud

8.3 Warm-up: The 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

8.4 Summary

8.5 References

9 Fighting frauds: 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

9.3 Summary

9.4 References

10 Fighting frauds: Social Network Analysis

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

10.5 Summary

10.6 References

11 Taming Text with Graphs

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

11.3 Summary

11.4 References

12 Knowledge Graphs






What's inside

  • The lifecycle of a machine learning project
  • Three end-to-end applications
  • Graphs in big data platforms
  • Data source modeling
  • Natural language processing, recommendations, and relevant search
  • Optimization methods

About the reader

Written for readers comfortable with machine learning basics.

About the author

Alessandro Negro is a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning.

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