Analyze Graph Data with Python
Workshop: analyze graphs in Python with the GDS client and Aura Graph Analytics. Run PageRank, Louvain, FastRP embeddings, and pathfinding at scale.
In this 4-hour workshop, you will learn
Analyze Graph Data with Python is the companion course to an instructor-led GraphAcademy workshop. Over roughly four hours, you move 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. out of Neo4j Browser and into Python: running Full definition for centrality (opens in a new tab)How important a node is within a graph. Each centrality algorithm defines importance differently., Full definition for community detection (opens in a new tab)A family of algorithms that group nodes by how they connect. Each algorithm defines a community differently., and Full definition for embedding (opens in a new tab)Information represented as a numerical vector, positioned so that similar information sits close together. algorithms on a citation network with the GDS Python client, then scaling up to 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. on a logistics dataset 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..
The premise is simple: most real-world data science happens in Python. The Graph Data Science (GDS) Python client wraps the same algorithms and the same project-run-write workflow you already know in a Pythonic interface that returns Pandas DataFrames, so your graph analytics results flow straight into the rest of your data science stack. Aura Graph Analytics then removes the infrastructure question entirely, running those algorithms in on-demand, session-based compute.
GDS Python Client
Install and configure the GDS Python client to connect to Neo4j and run graph algorithms programmatically from a Python environment.
Aura Graph Analytics
Use Neo4j Aura Graph Analytics to execute graph algorithms at scale without managing a dedicated GDS server or local infrastructure.
PageRank
Apply the PageRank algorithm to identify the most influential nodes in a graph and interpret the resulting scores for real-world datasets.
Betweenness Centrality
Compute betweenness centrality to discover nodes that act as critical bridges in a network and understand their role in information flow.
FastRP
Generate node embeddings with FastRP to represent graph structure as numerical vectors, enabling downstream machine learning tasks.
What you need to take part.
A GraphAcademy account
The workshop is delivered here on GraphAcademy, so you need to be signed in to work through the lessons and keep your progress. Creating an account is free.
A GitHub account, or Git on your own machine
You write code against the workshop-gds-python-aga repository. The quickest route is a GitHub Codespace, an online editor that clones the code and installs everything for you — that needs a GitHub account.
If you would rather work locally, clone the repository with Git and run it in your own editor instead. No GitHub account is needed for that.
2 modules, 4 hours.
Who this workshop is for
This course accompanies a live, instructor-led workshop: if you are attending one, work through the lessons alongside the instructor; if not, every step is in the course and you can complete it self-paced. It is for data scientists and analysts who have learned 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. basics in Neo4j Browser and want to work the way they normally do — in Python, with DataFrames and notebooks — and for engineers evaluating 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. for running graph algorithms at scale without managing GDS installations. You should first complete Graph Data Science in Practice, the preceding workshop, or have equivalent experience with 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 the project-run-write workflow, and be familiar with Python and Pandas DataFrames.
What you'll do
This is a hands-on workshop. You open a pre-configured GitHub Codespace containing the workshop repository and a ready-to-run Python environment, so there is nothing to install locally. From there, every lesson has you executing real code: connecting 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. Python client to Neo4j, projecting graphs, running algorithms, and reading the results back as DataFrames.
In the first half you analyze a citation network, scoring papers with 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. and 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., finding research communities with Full definition for Louvain algorithm (opens in a new tab)A community detection algorithm that repeatedly merges nodes into groups for as long as merging raises modularity., and generating Full definition for FastRP (opens in a new tab)A node embedding algorithm, short for Fast Random Projection. It builds each node's vector by combining random vectors drawn from the nodes around it. Full definition for node embedding (opens in a new tab)A list of numbers that stands in for a node's position in the graph, so that nodes in similar positions get similar lists. you can feed to downstream machine learning. In the second half you create ephemeral 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 and run Full definition for Dijkstra's algorithm (opens in a new tab)An algorithm that finds the cheapest route between two nodes.'s and Yen's 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. algorithms against the Cargo 2000 logistics dataset — five months of air cargo shipping data from a real freight forwarder — to find inefficiencies in its routing decisions.
Where to go next
The natural next step is Aura Graph Analytics fundamentals, which goes deeper on the session model you used in the final module. From there, Understand centrality algorithms sharpens the judgement side of the Full definition for centrality (opens in a new tab)How important a node is within a graph. Each centrality algorithm defines importance differently. work you did here. When you are ready to validate your skills, the Neo4j Graph Data Science certification covers 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., algorithm selection, and result interpretation across the categories you practised in both workshop modules. GraphAcademy also runs workshops on graph fundamentals, data import, and Go to glossary for generative AI (opens in a new tab)Models that produce new content rather than classifying or scoring content that already exists. throughout the year — find upcoming sessions on the workshops page.