Understand centrality algorithms
Master centrality graph algorithms in Aura Graph Analytics. Sort them into six families, then run each against a real network of 59,798 messages.
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?" — but they disagree about what "important" means. This lab teaches you to 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. (GDS) library into six families, so you always know which one answers the question you are actually asking.
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. Full definition for PageRank (opens in a new tab)A centrality algorithm that scores a node by the number of nodes pointing at it and by how important those nodes are. measures endorsement. CELF picks a group rather than a single node. Choosing between them is a modelling decision you make before any query runs, and getting it wrong means computing a precise answer to the wrong question. The six families spell TARGET: Ties, Articulation, Reach, Gatekeeping, Endorsement, and Teams. Each family encodes one definition of importance; the algorithms within it differ only in method.
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.
Who this course is for
This lab is for data scientists and analysts who can already run graph algorithms and now need to choose between them. If you have ever asked "should I use Full definition for PageRank (opens in a new tab)A centrality algorithm that scores a node by the number of nodes pointing at it and by how important those nodes are. or Betweenness here?", this is the course that gives you a principled answer. It also suits anyone analysing organizational, social, or communication networks, where "who matters most?" is the central question. You should be able to read an algorithm call, configure a 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., and work with 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. sessions before you start; the prerequisite courses are listed beside this overview.
What you'll do
This is a lab, so nearly all of your time is hands-on. GraphAcademy provisions an Go to glossary for Aura instance (opens in a new tab)A single Neo4j database running in Aura. for you, and you load a real communication network into it: 59,798 messages sent between 1,899 people. Every algorithm runs against this graph, so you can compare different Full definition for centrality (opens in a new tab)How important a node is within a graph. Each centrality algorithm defines importance differently. measures on the same data and see exactly where they disagree.
You will create an Full definition for Aura Graph Analytics session (opens in a new tab)A managed, temporary compute environment that holds a projection and runs algorithms against it., project the message graph into it, and then work through the six families one by one - from Ties (who is connected to the most people?) through Gatekeeping (who brokers the flow of information?) to Teams (which group is influential together?) - running representative algorithms from each and interpreting what the scores mean for the people in the network. The same person can rank near the top on one measure and nowhere on another, and understanding why is the point. The lab ends with a quiz where you match analytical questions to the right family, confirming you can make the choice unaided.
Where to go next
Full definition for centrality (opens in a new tab)How important a node is within a graph. Each centrality algorithm defines importance differently. is one category of graph algorithms; Full definition for pathfinding (opens in a new tab)A family of algorithms that find routes through a graph. What counts as the best route differs by algorithm. is another. Path Finding with GDS teaches shortest path algorithms on weighted and unweighted graphs, and pairs naturally with the Gatekeeping family, since betweenness is built from shortest paths. If you arrived here without the full grounding in 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. and execution modes, Get started with Graph Data Science fills those gaps. When you can choose a centrality family with confidence, you are well prepared for the Neo4j Graph Data Science certification, which tests exactly this kind of judgement: selecting, configuring, and interpreting graph algorithms against real analytical questions.