Enterprise Database Systems
Graph Data Science with Neo4j
Neo4j: Applying Graph Algorithms on In-memory Graphs
it_dagdsndj_03_enus
Neo4j: Building Graphs with Neo4j's Graph Data Science Library
it_dagdsndj_01_enus
Neo4j: Managing Graphs with the Graph Data Science Library
it_dagdsndj_02_enus
Neo4j: Applying Graph Algorithms on In-memory Graphs
Lesson Objectives
Neo4j: Applying Graph Algorithms on In-memory Graphs
- discover the key concepts covered in this course
- create nodes and relationships from the contents of CSV files
- use different algorithms from the Graph Data Science library to compute the importance of each node in terms of connections
- identify clusters of closely knit communities in a network
- find individual nodes or clusters of nodes in a network which are not connected to one another
- compare and contrast the different techniques available to measure the importance of page references in a network of links
- create a graph where each relationship has an attached weight
- find the shortest path between two nodes in network using the implementation of Dijkstra's algorithm in the Graph Data Science library
- use variant's of Dijkstra's algorithm to find multiple paths between the nodes in a network
- perform a breadth-first and depth-first traversal of a graph
- represent each node in your graph as a vector defined in a specified number of dimensions
- summarize the key concepts covered in this course
Overview/Description
This course will introduce you to several graph algorithms in Neo4j's Graph Data Science library and explore how you can apply these to different types of graphs. You begin by building a little social network of people connected as friends. Then you will cover the steps involved in modeling friendships as undirected relationships in an in-memory graph and applying algorithms to this social network. You will use measures of centrality to identify highly connected nodes in a network. Next, you dive into community detection algorithms to find clusters of friends in a social network. From there, you will model a network as a graph with weighted edges then apply traversal algorithms on this graph, from finding shortest paths between nodes to breadth-first and depth-first traversals. Finally, you get a glimpse into the FastRP algorithm to transform nodes in your graph to vectors with a specific number of dimensions. After completing this course, you will know how to apply various graphic algorithms to extract meaningful information from a graph.
This course will introduce you to several graph algorithms in Neo4j's Graph Data Science library and explore how you can apply these to different types of graphs. You begin by building a little social network of people connected as friends. Then you will cover the steps involved in modeling friendships as undirected relationships in an in-memory graph and applying algorithms to this social network. You will use measures of centrality to identify highly connected nodes in a network. Next, you dive into community detection algorithms to find clusters of friends in a social network. From there, you will model a network as a graph with weighted edges then apply traversal algorithms on this graph, from finding shortest paths between nodes to breadth-first and depth-first traversals. Finally, you get a glimpse into the FastRP algorithm to transform nodes in your graph to vectors with a specific number of dimensions. After completing this course, you will know how to apply various graphic algorithms to extract meaningful information from a graph.
Target
Prerequisites: none
Neo4j: Building Graphs with Neo4j's Graph Data Science Library
Lesson Objectives
Neo4j: Building Graphs with Neo4j's Graph Data Science Library
- discover the key concepts covered in this course
- list the main categories of graph algorithms and recall their use cases
- recognize the mechanism of creating and working with graphs in Neo4j's Graph Data Science library
- install the Graph Data Science library for a Neo4j DBMS
- create an in-memory graph using the native projection configuration for nodes and relationships
- use the page rank algorithm to compute a score for each node in a graph
- load properties from a source database to an in-memory graph
- apply a Graph Data Science function to read a property from a graph
- build a graph using the Cypher projection by setting node and relationship queries
- create a Cypher projection graph with properties from the source database
- build a sub-graph containing a subset of elements from an already existing graph
- summarize the key concepts covered in this course
Overview/Description
The Graph Data Science (GDS) library provides data scientists and developers with the necessary tools to perform powerful analysis of their graph data. In this course, you will look at various use cases of GDS and cover some of its essential operations. Begin with an overview of the GDS library and then dive into using the library by building in-memory graphs from the contents of your Neo4j database. Explore how to build graphs using native and Cypher projections. Next, apply a graph algorithm to your GDS graph and see how it can be used to obtain meaningful information about the nodes and relationships in your data. After completing this course, you will have a fundamental understanding of the GDS library for Neo4j and the necessary capabilities to build your own in-memory graphs and extract significant insights.
The Graph Data Science (GDS) library provides data scientists and developers with the necessary tools to perform powerful analysis of their graph data. In this course, you will look at various use cases of GDS and cover some of its essential operations. Begin with an overview of the GDS library and then dive into using the library by building in-memory graphs from the contents of your Neo4j database. Explore how to build graphs using native and Cypher projections. Next, apply a graph algorithm to your GDS graph and see how it can be used to obtain meaningful information about the nodes and relationships in your data. After completing this course, you will have a fundamental understanding of the GDS library for Neo4j and the necessary capabilities to build your own in-memory graphs and extract significant insights.
Target
Prerequisites: none
Neo4j: Managing Graphs with the Graph Data Science Library
Lesson Objectives
Neo4j: Managing Graphs with the Graph Data Science Library
- discover the key concepts covered in this course
- add properties to an in-memory graph based on the computation of an algorithm
- apply the degree centrality algorithm on a graph to get the level of connectedness of each node
- use the write function of a graph algorithm to publish the results of a computation to the underlying nodes of the database
- persist an in-memory graph to a Neo4j database
- load properties from the source database of a graph when exporting it to a new database
- remove graphs from the graph catalog
- export in-memory graphs to a set of CSV files containing data for nodes and relationships
- summarize the key concepts covered in this course
Overview/Description
This course will teach you to use operations that modify in-memory graphs built with the Graph Data Science (GDS) library, update the properties of the underlying database, and export their contents to a database or file. You begin with a mutate operation to add new properties to an in-memory graph. You will learn how you can save the results of graph algorithms in such graphs for later reference. Next, you explore the write operation to update the underlying database with the results of graph algorithms on in-memory graphs. You will then move on to exporting your graph to a persistent store. Finally, you cover the different ways you can remove GDS graphs from the graph catalog. While doing so, you will explore the degree centrality calculation, which measures how well-connected nodes are in a network. After completing this course, you will have a fundamental understanding of how to administer in-memory graphs using the Graph Data Science library in Neo4j.
This course will teach you to use operations that modify in-memory graphs built with the Graph Data Science (GDS) library, update the properties of the underlying database, and export their contents to a database or file. You begin with a mutate operation to add new properties to an in-memory graph. You will learn how you can save the results of graph algorithms in such graphs for later reference. Next, you explore the write operation to update the underlying database with the results of graph algorithms on in-memory graphs. You will then move on to exporting your graph to a persistent store. Finally, you cover the different ways you can remove GDS graphs from the graph catalog. While doing so, you will explore the degree centrality calculation, which measures how well-connected nodes are in a network. After completing this course, you will have a fundamental understanding of how to administer in-memory graphs using the Graph Data Science library in Neo4j.
Target
Prerequisites: none
