🔴 CLOSED Round of 32 Challenge: GDS 32! July 2–8, 2026

:trophy: Round of 32 Challenge: The Rule of 32 - July 2–8, 2026

1. Complete the Graph Data Science Learning Path


:backhand_index_pointing_right: GraphAcademy Learning Paths

Before you can participate in the Round of 32 Challenge, complete the following GraphAcademy learning path:

:books: Graph Data Science

Required courses:

  • :white_check_mark: Get Started with Graph Data Science
  • :white_check_mark: Path Finding with GDS

The Neo4j Graph Data Science Certification is optional and is not required for this challenge.


2. Build a Small Project

Using what you learned in the Graph Data Science learning path, build a small Neo4j project that demonstrates one or more concepts from the courses.

:bullseye: The Rule of 32

To keep the challenge fun and accessible, your project must follow these limits:

  • :white_check_mark: Maximum 32 nodes
  • :white_check_mark: Maximum 32 relationships
  • :white_check_mark: A project description between 32–320 words

Your project can use:

  • A GraphAcademy dataset
  • A Neo4j sample dataset
  • Your own dataset
  • A dataset you create yourself

The goal isn't to build something large—it's to showcase what you learned in a creative way.


Example Submission

I completed the Graph Data Science learning path and built a small social network graph to explore how people are connected. My graph contains 18 nodes and 27 relationships. Using techniques from the learning path, I explored how connections between people could reveal useful insights for recommending new friends or identifying highly connected individuals. This project helped me better understand how graph data can uncover relationships that are difficult to see in traditional tables.


:stadium: Round of 32

Submission Period: July 2–8, 2026

:wrapped_gift: Winner's Choice

  • Cristiano Ronaldo CR7 Celebration Display
  • Lionel Messi #10 Celebration Display

Prize Eligibility

To qualify for the Round of 32:

  • Complete the required Graph Data Science learning path.
  • Submit an original project built for this challenge.
  • Include your public GraphAcademy profile.
  • Follow the Rule of 32 project limits.
  • Previous submissions may not be resubmitted.

:trophy: GraphAcademy Cup Submission

Topic Title Format

Round of 32: Project Name – Country


GraphAcademy Cup Team Profile Link

Paste the URL to your country team page.

Team Profile Link:


GraphAcademy Public Profile Username

Public Profile Username:

:warning: Anonymous GraphAcademy profiles are not eligible for weekly LEGO prizes.


Country

Country:


Learning Path Completed

Learning Path:

Graph Data Science


Project Name

Project Name:


Project Description

Tell us what you built and how it demonstrates what you learned from the Graph Data Science learning path.

Project Description (32–320 words):


Screenshots

Upload 1–3 screenshots of your graph or project.


Repository / Demo Link (Optional)

GitHub / Demo URL (Optional):


Additional Notes (Optional)

Anything else you'd like the community to know?

Additional Notes:


:white_check_mark: Submission Checklist

Before submitting, confirm that:

  • ☐ I completed the Graph Data Science learning path.
  • ☐ I am participating in the GraphAcademy Cup.
  • ☐ I included my Team Profile Link.
  • ☐ I included my GraphAcademy Public Profile Username.
  • ☐ My GraphAcademy profile is public and eligible for prize verification.
  • ☐ My project contains 32 nodes or fewer.
  • ☐ My project contains 32 relationships or fewer.
  • ☐ I included 1–3 screenshots.
  • ☐ My project description is 32–320 words.

:scroll: Contest Reminder

:wrapped_gift: One LEGO prize winner will be selected from the Round of 32 submissions based on creativity, application of Graph Data Science concepts, and overall presentation.

:scroll: Terms & Conditions

GraphAcademy Cup
Terms & Conditions | GraphAcademy Cup

:red_question_mark: FAQ

GraphAcademy Cup
FAQ | GraphAcademy Cup

Topic Title Format Round of 32: El Clásico Counter-Attack Optimizer – India

GraphAcademy Cup Team Profile Link: India | GraphAcademy Cup

GraphAcademy Public Profile Username: shubhamsingh.41188

Country: India

Learning Path Completed: Graph Data Science

Project Name: El Clásico Counter-Attack Optimizer

Project Description (32–320 words): The graph uses 10 nodes to represent tactical points on the pitch: the defensive zone, iconic playmakers (like Xavi, Iniesta, Kroos, and Modric), the star forwards, and the goal itself. I created 15 relationships to map out the actual passing lanes and shots, weighting each connection by the execution time in seconds.

1. Nodes Table (Total: 10 Nodes):

Node Type (Label) Name (Property) Team / Role Description
Zone Defensive Zone Start Point This is the exact starting point where the counter-attack begins.
Player Toni Kroos Real Madrid Deep playmaker (Midfielder)
Player Luka Modric Real Madrid Creative playmaker (Midfielder)
Player Karim Benzema Real Madrid Link-up striker (Forward)
Player Cristiano Ronaldo Real Madrid Target finisher (Forward)
Player Xavi Hernandez Barcelona Maestro playmaker (Midfielder)
Player Andres Iniesta Barcelona Magician playmaker (Midfielder)
Player Luis Suarez Barcelona Dangerous striker (Forward)
Player Lionel Messi Barcelona Ultimate finisher (Forward)
Target Goal End Point Back of the net.

2. Relationships Table (Total: 15 Links)

From (Ball Start) Action (Type) To (Ball Receive) Time (Seconds) Tactical Route
Defensive Zone PASS_TO Toni Kroos 1.4s Real Madrid Transition
Defensive Zone PASS_TO Xavi Hernandez 1.6s Barcelona Transition
Toni Kroos PASS_TO Luka Modric 1.1s Real Madrid Midfield Flow
Toni Kroos PASS_TO Karim Benzema 2.2s Real Madrid Long Pass
Luka Modric PASS_TO Cristiano Ronaldo 0.9s Real Madrid Key Pass
Karim Benzema PASS_TO Cristiano Ronaldo 1.2s Real Madrid Assist
Luka Modric PASS_TO Karim Benzema 1.8s Real Madrid Buildup
Cristiano Ronaldo SHOOT Goal 0.4s Real Madrid Clinical Finish
Xavi Hernandez PASS_TO Andres Iniesta 1.0s Barcelona Tiki-Taka
Xavi Hernandez PASS_TO Luis Suarez 2.1s Barcelona Through Ball
Andres Iniesta PASS_TO Lionel Messi 0.8s Barcelona Key Pass
Luis Suarez PASS_TO Lionel Messi 1.1s Barcelona Assist
Andres Iniesta PASS_TO Luis Suarez 1.9s Barcelona Buildup
Lionel Messi SHOOT Goal 0.3s Barcelona Clinical Finish
Toni Kroos PASS_TO Cristiano Ronaldo 2.8s Cross-field Long Diagonal Ball

Using the core concepts from the "Path Finding with GDS" course, I projected this network into memory and ran Dijkstra’s Shortest Path algorithm. The goal was to find the absolute fastest route to progress the ball from defense to the back of the net. It perfectly shows how graph data science can optimize spatial routing and real-time decision-making in a way that traditional database tables just can't match.

Screenshots:

Country: India
Public Profile:
Prashant Sable | GraphAcademy**
Learning Path Completed: Yes -** Get started with Graph Data Science Certificate | GraphAcademy

**
Team Profile Link:** India | GraphAcademy**

Project Name: Disease–Symptom–Treatment Knowledge Graph** -
Project Description:

This project creates a Disease–Symptom–Treatment Knowledge Graph in Neo4j to model relationships between diseases, symptoms, treatments, and affected body systems. It includes diseases such as Flu, Pneumonia, Asthma, Migraine, Diabetes, and Hypertension, connected to medical concepts like Fatigue, Headache, Cough, Antibiotics, and Respiratory.

Screenshot with details:
Total Nodes: 28, Total Relationships: 32

Degree Centrality:
Degree centrality identified the nodes with the highest number of direct connections in the graph. It highlighted common shared medical concepts such as **Fatigue
**

PageRank:
PageRank helped find the most influential nodes in the graph, not just the most connected ones. It showed that nodes like Fatigue, Flue, and Diabetes are important because they connect multiple key diseases and act as central medical concepts.

Betweenness Centrality:
Betweenness centrality identified bridge nodes that connect different disease groups

Louvain Community Detection:
Louvain grouped the graph into communities based on dense internal connections.

Label Propagation:
Label Propagation also detected communities in the graph and provided a second clustering view. It supported the Louvain results by showing that related diseases and symptoms tend to form stable groups based on shared medical relationships.

Node Similarity: Node Similarity was used to find diseases that are structurally similar because they share symptoms, treatments, or affected body systems

Dijkstra’s Shortest Path:Dijkstra’s algorithm was used to find the shortest path between two diseases in the graph. It helped explain how diseases are related through intermediate nodes such as common symptoms or body system.

Topic Title: Round of 32: Apple Supply Chain Concentration Risk Graph – India :india:

GraphAcademy Cup Team Profile Link: Neo4j team member

GraphAcademy Public Profile Username: Lvyapari

Country: India

Learning Path Completed:

Project Name: Apple Supply Chain Concentration Risk Graph


Project Description (32–320 words) — currently ~185 words

Apple publishes an official Supplier List every year covering 98% of its direct manufacturing spend. I modeled 24 real suppliers and the 8 countries where they manufacture — every name copied verbatim from Apple's own PDF — as a 32-node graph (8 Country + 24 Supplier, 32 MANUFACTURES_IN relationships).

Apple has publicly discussed reducing China-manufacturing concentration risk for years. This graph makes that risk measurable instead of anecdotal: degree centrality shows China mainland holds 59% of all connections in the sample — the next closest country holds 6%.

Betweenness centrality surfaces exactly which suppliers already have multi-country footprints (Flex Limited, Delta Electronics) — the real answer to "who could absorb volume if one region is disrupted." Louvain independently rediscovers regional clusters — Taiwan paired with Thailand, Vietnam paired with India and the US — bridged by those same suppliers, with no region ever labeled by hand. Dijkstra answers the operational question directly: the shortest real bridge from Taiwan to Thailand is one supplier, Delta Electronics, two hops.

A spreadsheet lists suppliers. Only a graph answers "which supplier is the shortest bridge between two manufacturing bases."


Screenshots:

  1. Concentration Risk (Degree Centrality)

  2. Bridging Suppliers (Betweenness Centrality)

  3. Supply Chain Resilience Route (Dijkstra)

GitHub / Demo URL: GitHub - luckyvyapari/apple-supply-chain-graph: A live Graph Data Science dashboard analyzing concentration risks, bridge routing, regional community clusters, and supplier similarity in Apple's supply chain using Neo4j GDS. · GitHub

Dataset URL: Official Apple Supplier List (PDF)

Additional Notes: Dataset is Apple's own public disclosure (FY2023 Supplier List, 98% of direct spend), not synthetic. Node/edge counts and all four GDS results verified live against a local Neo4j 5 + GDS 2.13.11 instance on 2026-07-07.