Understand centrality algorithms
Centrality algorithms can be divided into six loose families, and the algorithms inside a family answer similar questions. In this lab, you'll understand which families are the right ones for which kinds of analyses.
In this 2-hour course, you will learn
Full definition for centrality (opens in a new tab)How important a node is within a graph. Each centrality algorithm defines importance differently. algorithms all answer the question "which Full definition for node (opens in a new tab)A vertex in a graph. In a property graph it can carry labels and properties. are the most important?"
They disagree about what "important" means. Full definition for degree centrality (opens in a new tab)A score for each node equal to its number of outgoing relationships. counts Full definition for relationship (opens in a new tab)A named, directed connection between two nodes. Every relationship has a type, a start node and an end node.. Full definition for betweenness centrality (opens in a new tab)A score for each node equal to how often it lies on the shortest paths between other nodes. counts shortest paths. CELF picks a group rather than a single node. Choosing between them is a modelling decision you make before any query runs.
In this lab, you'll sort every centrality algorithm in Go to glossary for Aura Graph Analytics (opens in a new tab)The Aura service that runs graph algorithms in a separate session, with no plugin to install. and the Go to glossary for graph data science (opens in a new tab)Analysing data through the structure of its connections. Also the name of the Neo4j library that implements it. library into six families, spelling TARGET: Ties, Articulation, Reach, Gatekeeping, Endorsement and Teams.
You will work with a real network of 59,798 messages sent between 1,899 people, running algorithms from each family against that graph.
By the end, you will understand how each algorithm family defines "importance" and how to implement these algorithms in your data science practice.
Before taking this course, you should have:
- Completed Get started with Graph Data Science, or equivalent experience with the GDS algorithm catalog
- Completed Aura Graph Analytics fundamentals, or equivalent experience with sessions and Full definition for projection (opens in a new tab)An in-memory copy of part of your database that graph algorithms run against. You choose which nodes and relationships it holds.
Centrality is a set of questions
Each family encodes a different definition of important. Choose a centrality family before choosing your algorithm.
Six families, spelling TARGET
Ties, Articulation, Reach, Gatekeeping, Endorsement and Teams. Every centrality algorithm in Aura Graph Analytics belongs to one of them.
A family agrees with itself
Each algorithm in one family ultimately agree with each other on definitions. They differ in their precise methods to reach produce relevant analyses.