🔴 CLOSED: Week 3 Challenge: Show What You Learned

[Ari_Neo4j (Neo4j Community Manager)]

Week 3 Challenge: Show What You Learned

1. Join the GraphAcademy Cup

Before you can participate in the Community Challenge, you'll need to join the GraphAcademy Cup and represent your country.

Join the GraphAcademy Cup

Register for the GraphAcademy Cup and join your country team. (must be done before you complete the course)

2. Complete a GraphAcademy Course

Complete one of the following GraphAcademy courses:

• Cypher Fundamentals
• Importing Data Fundamentals
• Neo4j & GenAI Fundamentals
• Graph Data Modeling Fundamentals
• Neo4j Fundamentals
• Developing with Neo4j MCP Tools

GraphAcademy Courses

Then submit:

Your Team Profile Link

Your GraphAcademy Public Profile Username

The course you completed

One screenshot from GraphAcademy showing completion

A short explanation (100–300 words) answering:

What was the most valuable thing you learned, and how could you apply it to a real-world problem?

Example Submission

I completed Cypher Fundamentals. The most valuable concept I learned was pattern matching using MATCH. I could apply this to identify shared entities in a fraud investigation graph, helping investigators discover hidden relationships more quickly than traditional SQL queries.

Prize Eligibility

• Must complete one of the eligible courses during Week 2.
• Must include a public GraphAcademy profile.
• Must include Team URL and Profile Username.
• Previous submissions may not be resubmitted.

This lowers the barrier dramatically:

• No coding required.
• No GitHub required.
• No project build required.
• Still reinforces learning.
• Easy for you to review and verify.# GraphAcademy Cup Submission

Topic Title Format:
Week 1: Project Name - Country

GraphAcademy Cup Team Profile Link

Paste the URL to your country team page:

Team Profile Link:

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GraphAcademy Public Profile Username

Public Profile Username:

Anonymous GraphAcademy profiles are not eligible for weekly LEGO prizes.

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Country

Country:

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GraphAcademy Course Completed

Course Name:

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Project Name

Project Name:

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Description

Tell us what you built.

Description:

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What Did You Learn?

What concepts, techniques, or lessons from GraphAcademy did you apply?

What I Learned:

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Screenshot

Drag and drop screenshots below.

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Repository / Demo Link

GitHub / Demo URL (Optional):

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Additional Notes

Anything else you'd like the community to know?

Additional Notes:

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Submission Checklist

Before submitting, confirm that:

• I am participating in the GraphAcademy Cup.

• I included my Team Profile Link.

• I included my GraphAcademy Public Profile Username.

• My profile is public and eligible for prize verification.

• My project is related to concepts learned in GraphAcademy.

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Contest Reminder

One LEGO prize winner will be selected every week during the GraphAcademy Cup.

Terms & Conditions:

Terms & Conditions | GraphAcademy Cup

FAQ:

FAQ | GraphAcademy Cup

[leongkwokhing]

Week 3: IT Resilience Graph – Singapore

GraphAcademy Cup Team Profile Link: Singapore | GraphAcademy Cup

GraphAcademy Public Profile Username: leongkwokhing

Country: Singapore

GraphAcademy Course Completed Course Name: Graph Data Modeling Fundamentals

Project Name: IT Resilience Graph

Description: This project leverages Neo4j Aura to build a dynamic IT infrastructure dependency model that unifies hardware, network, and application layers into a single knowledge graph. By mapping connections between critical internal assets and vendor-provided resources (such as Stripe and Singtel), the graph replaces traditional, siloed asset tracking with interconnected intelligence.

Designed for high-stakes operational resilience, the solution enables IT teams to perform instant blast-radius assessments and proactive risk management. For instance, teams can execute multi-tier variable-length path queries to instantly trace how a hardware failure cascades up to mission-critical applications, or identify how a single external vendor outage (like a Singtel telecom disruption) propagates across organisational boundaries to compromise 8 distinct downstream IT assets.

What Did You Learn?: The course provided a reinforce my understanding of graph theory and graph database architecture. Moving beyond the limitations of rows and columns, I learned how nodes, relationships and properties form an expressive, interconnected web capable of mirroring real-world complexities. The course's deep dive into common graph use cases equipped me with the logical framework needed to model intricate data ecosystems. Furthermore, mastering Cypher pattern matching unlocked the ability to turn raw data points into actionable insights.

I directly applied these concepts to build the IT Resilience Graph, transforming traditional, siloed IT asset tracking into an interconnected intelligence model. By organising hardware, network, and application components as distinct nodes and defining their relationships, I successfully mapped a dynamic topology across internal and external vendor boundaries. Applying the graph modelling and pattern-matching techniques learned in the course allowed me to implement the graph schema variable-length path queries in Neo4j Aura. This capability enables IT teams to perform precise blast-radius assessments during live incidents, tracing how a single localised failure—such as a hardware crash or a Singtel telecom outage – cascades across architectural layers to compromise critical upstream applications.

Screenshot
Subgraph in Neo4j depicting the dependency between IT assets:

[Impact Analysis] If the SAN Storage Array experiences a hardware crash, using
variable-length paths (*) to traverse multiple tiers of infrastructure layers instantly
reveal that both the Customer Database and Oracle Database (and sequentially, the
E-Commerce site and ERP) will be affected.

[Vendor Risk Management] By tracing paths across external boundaries, the graph
reveals that a single Singtel telecom outage triggers a domino effect impacting 8
distinct IT assets. This showcases Neo4j's unique ability to map cross-organisational
dependencies for proactive vendor risk management.

Additional Notes: While path traversal shows what is currently affected, we can explore using Neo4j’s Graph Data Science (GDS) library to calculate hidden infrastructure risks before an outage happens.

For example, running the Betweenness Centrality or PageRank algorithm on the (:Component) nodes. This could pinpoint the most critical infrastructure bottlenecks (e.g., a specific router or database) that have the highest number of shortest paths passing through them. If these nodes go down, the largest chunk of your infrastructure goes down.

[sekharchennaiah12345 (Sekharchennaiah12345ctk)]

I made it as seperate post in related topic channel. Later get to know that we need to post here. Here is created post link (Week 3: KiranaAI (Text2Cypher + Text2SQL AI Agent for Kirana Stores - India) - 🇮🇳)

[Ari_Neo4j (Neo4j Community Manager)]

It's all good. They should be a separate post buy your entry is in

[xlrtaha (Taha Benbrahim)]

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Week 3: VEIL – Graph-Native Corporate Relationship Platform - Morocco

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GraphAcademy Cup Team Profile Link

Team Profile Link: Morocco | GraphAcademy Cup

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Public Profile Username:

xlrtaha

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Country

Morocco

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GraphAcademy Courses Completed

Cypher Fundamentals, Neo4j Fundementals and Neo4j Certified Professional

Certificates Screenshot

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Project Name

VEIL – Graph-Native Corporate Relationship Platform

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Description

VEIL is a graph-native intelligence platform for exploring corporate ownership networks using live UK Companies House data.

The platform ingests companies, directors, officers, and Persons with Significant Control (PSCs) into a Neo4j graph database and exposes the relationships through an interactive Streamlit interface. Users can explore ownership structures, visualize connected entities, analyze graph topology, inspect influence metrics, execute configurable Cypher queries, and navigate corporate ecosystems through an interactive dashboard.

The architecture is modular, allowing the registry connector to be replaced with another corporate registry while preserving the graph model and analytics pipeline.

Built with Neo4j, Python, and Streamlit.

This project was vibe-coded, guided by technical knowledge built through certifications and practical exploration. Rather than writing every line of code manually, I used AI as an engineering multiplier while focusing on architecture, system design, iterative prompting, validation, debugging, and continuous refinement.

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What I Learned

The most valuable lesson from GraphAcademy was learning how to model real-world relationships as a graph instead of forcing them into relational tables. Concepts such as nodes, relationships, graph schema design, and efficient Cypher pattern matching fundamentally changed how I approached entity resolution and ownership analysis.

I applied these concepts directly while developing VEIL by modeling companies, directors, officers, and PSCs as interconnected graph entities. This made it possible to traverse ownership structures naturally, detect shared directors, identify corporate communities, and compute graph analytics that would be significantly more complex using traditional SQL joins.

Working with Neo4j also reinforced the importance of designing the graph around relationships rather than records. This project demonstrated how graph databases can simplify investigations involving beneficial ownership, financial crime, compliance, and corporate intelligence, while providing an intuitive way to explore highly connected datasets.

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Devpost Publication Link with Demo Video:

ubo-knowledge-graph

Screenshots