封面
版权信息
About Packt
Why subscribe?
Contributors
Preface
Section 1: Graph Modeling with Neo4j
Graph Databases
Graph definition and examples
Moving from SQL to graph databases
Neo4j – the nodes relationships and properties model
Understanding graph properties
Considerations for graph modeling in Neo4j
Summary
Further reading
The Cypher Query Language
Technical requirements
Creating nodes and relationships
Updating and deleting nodes and relationships
Pattern matching and data retrieval
Using aggregation functions
Importing data from CSV or JSON
Measuring performance and tuning your query for speed
Summary
Questions
Further reading
Empowering Your Business with Pure Cypher
Technical requirements
Knowledge graphs
Graph-based search
Recommendation engine
Summary
Questions
Further reading
Section 2: Graph Algorithms
The Graph Data Science Library and Path Finding
Technical requirements
Introducing the Graph Data Science plugin
Understanding the importance of shortest path algorithms through their applications
Dijkstra's shortest paths algorithm
Finding the shortest path with the A* algorithm and its heuristics
Discovering the other path-related algorithms in the GDS plugin
Optimizing processes using graphs
Summary
Questions
Further reading
Spatial Data
Technical requirements
Representing spatial attributes
Creating a geometry layer in Neo4j with neo4j-spatial
Performing spatial queries
Finding the shortest path based on distance
Visualizing spatial data with Neo4j
Summary
Questions
Further reading
Node Importance
Technical requirements
Defining importance
Computing degree centrality
Understanding the PageRank algorithm
Path-based centrality metrics
Applying centrality to fraud detection
Summary
Exercises
Further reading
Community Detection and Similarity Measures
Technical requirements
Introducing community detection and its applications
Detecting graph components and visualizing communities
Running the Label Propagation algorithm
Understanding the Louvain algorithm
Going beyond Louvain for overlapping community detection
Measuring the similarity between nodes
Summary
Questions
Further reading
Section 3: Machine Learning on Graphs
Using Graph-based Features in Machine Learning
Technical requirements
Building a data science project
The steps toward graph machine learning
Using graph-based features with pandas and scikit-learn
Automating graph-based feature creation with the Neo4j Python driver
Summary
Questions
Further reading
Predicting Relationships
Technical requirements
Why use link prediction?
Creating link prediction metrics with Neo4j
Building a link prediction model using an ROC curve
Summary
Questions
Further reading
Graph Embedding - from Graphs to Matrices
Technical requirements
Why do we need embedding?
Adjacency-based embedding
Extracting embeddings from artificial neural networks
Graph neural networks
Going further with graph algorithms
Summary
Questions
Further reading
Section 4: Neo4j for Production
Using Neo4j in Your Web Application
Technical requirements
Creating a full-stack web application using Python and Graph Object Mappers
Understanding GraphQL APIs by example – GitHub API v4
Developing a React application using GRANDstack
Summary
Questions
Further reading
Neo4j at Scale
Technical requirements
Measuring GDS performance
Configuring Neo4j 4.0 for big data
Summary
Other Books You May Enjoy
更新时间：2021-06-11 18:50:48
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