30DaysOfGraph

A community learning challenge by GraphGeeks for people to learn about graphs

Welcome to #30DayOfGraphChallenge: a hands-on, learn-in-public challenge designed to help you understand graphs, knowledge graphs, and graph data modeling, from first principles to real-world applications.

This challenge is organized by GraphGeeks and is open to:

• Graph beginners
• Data engineers & analysts
• Software engineers
• Knowledge graph practitioners
• Anyone curious about graph thinking

---

Goal

By the end of 30 days, you will:

• Think in graphs, not tables
• Understand knowledge graphs
• Model data using graph-native approaches
• Work with Label Property Graphs (LPG) and RDF
• Apply graph algorithms
• Share your learning publicly

---

How This Challenge Works

• 📆 30 days, 1 topic per day
• 🧩 Concept-first, tool-second
• 📂 Daily folders in this repo
• 🔁 One Pull Request (PR) per day
• 🌍 Share your learning on LinkedIn using #30DaysOfGraph

---

How to Participate

1. Fork this repository
2. Clone your fork

   git clone https://github.com/<your-username>/30DaysOfGraph.git

3. Create a new branch for each day

   git checkout -b day-01

4. Add your work for the day (notes, diagrams, queries, explanations)
5. Commit and open a Pull Requests
6. Share what you learned on linkedin using the following to get reposted:

      #30DayGraphChallenge #GraphGeeks @GraphGeeksOrg
   

📅 30 Day of Graph Challenge Plan

#30DayGraphChallenge

Phase 1: Graph Thinking & Foundations (Days 1–7)

Goal: rewire how people think about data

Day	Name	Description	Activity
Day 1	What is Graph Engineering?	- What problems graphs solve - Graph engineer vs data engineer vs data scientist - Real-world examples (fraud, recommendations, knowledge graphs)	Write 3 problems you’ve seen that involve relationships
Day 2	Graph Thinking	- Thinking in relationships instead of tables - Nodes vs Joins - Paths as first-class citizens	Take a real-world problem and describe it as nodes and relationships
Day 3	Graph Theory (Just Enough)	What is a graph? Nodes, edges, paths, cycles Directed vs undirected graphs	Draw the same graph once as directed and once as undirected
Day 4	Properties & Attributes	What are nodes properties? What are relationship properties? When data belongs on nodes vs edges?	Add properties to your Day 3 graph
Day 5	Types of Graphs	You will learn the difference between: - Directed vs undirected - Weighted graphs - Bipartite graphs - Multigraphs	Identify which graph type best fits your use case
Day 6	What is Linked Data	- URIs - Global identifiers - Linking across datasets	Find 3 real-world identifiers (ISBN, ORCID, Wikidata ID)
Day 7	What is a Knowledge Graph?	- Definition - How knowledge graphs differ from “just graphs” - Semantics, meaning, context (Knowledge graphs in search & AI)	Explain a knowledge graph in one paragraph (non-technical)

Phase 2: Graph Data Modeling (Shared Core) (Days 8–14)

Goal: learn modeling BEFORE technology

Day	Name	Description	Activity
Day 8	Graph Data Modeling Basics	- Entities vs relationships - Cardinality - Direction matters	Create a conceptual graph model (no tools)
Day 9	Modeling Relationships	- Verb-based relationships - Avoiding “has_*” anti-patterns - Modeling Events vs facts	Refactor weak relationships into strong ones
Day 10	Modeling Nodes	- What deserves its own node? - When to split vs merge entities - Super nodes & anti-patterns	Identify potential super nodes in your model
Day 11	Normalization in Graphs	What normalization means for graphs What applies from relational theory and does not apply Why over-normalization hurts graphs	Normalize a flat dataset into a graph
Day 12	Introducing LPG vs RDF (Conceptual)	Label Property Graphs vs RDF (triples) Similarities and differences Same graph, different representations	Map your conceptual model to LPG and RDF on paper
Day 13	Choosing LPG or RDF	When LPG fits better When RDF fits better Trade-offs (Performance, reasoning, semantics) Schema vs ontology	Decide which model fits your use case and why
Day 14	Shared Dataset	Introduce challenge dataset (movies, books, research, etc.) Understanding/Explain the domain & question you wish to answer	Write 5 questions you want to answer with the graph

Phase 3: LPG & RDF Paths (Days 15–20)

Goal: hands-on modeling & querying

Day	Name	Description	Activity
Day 15	LPG Modeling	Labels Relationship types Properties Schema-less vs schema-guided	Model the dataset as a Label Property Graph
Day 16	Cypher Basics	MATCH, CREATE, MERGE Pattern matching Traversals	Write 5 Cypher queries
Day 17	RDF Modeling	Triples (subject–predicate–object) URIs Vocabularies	Model the same dataset as RDF triples
Day 18	SPARQL Basics	SELECT & WHERE Filters Basic graph patterns	Write 5 SPARQL queries
Day 19	LPG vs RDF Queries	Expressiveness Verbosity Readability	Answer the same question in Cypher and SPARQL
Day 20	Semantics & Reasoning	What RDF gives you that LPG doesn’t (by default) Ontologies Inference	Add simple inference to your model (e.g subclass)

Phase 4: Graph Algorithms & Applications (Days 21–26)

Goal: extract value from graphs (You may implement this using a graph database or engine or networkX)

Day	Name	Description	Activity
Day 21	Introduction to Graph Algorithms	Why algorithms matter Local vs global algorithms	Identify where algorithms add value
Day 22	Centrality	Degree Betweenness PageRank	Find the most important nodes in your dataset
Day 23	Communities & Similarity	Community detection(Clustering) Node Similarity Grouping	Detect communities in your graph
Day 24	Path Finding	Shortest paths Variable-length traversals	Find meaningful paths
Day 25	Graph Analytics vs Transactions	OLTP vs OLAP Transactional vs analytical graphs When to use graph analytics engines	Classify your use case
Day 26	Graph + ML / AI	Graph Embeddings Graphs in recommender systems knowledge graphs in AI GraphRAG	Identify an AI use case for your graph

Phase 5: Real World & Wrap-Up (Days 27–30)

Goal: production mindset & inspiration

Day	Name	Description	Activity
Day 27	Data Quality iin Graphs	Duplicates Rogue nodes Inconsistent relationships	Find and fix bad data
Day 28	Scaling Graphs & Performance	Indexing Traversal depth Modeling for performance	Optimize one slow query
Day 29	Real-World Use Cases	Fraud Recommendations Knowledge graphs in enterprise	Map your graph to a real use case
Day 30	Becoming a Graph Engineer / Scientist	Skills roadmap Tooling ecosystem Community	Share your final model and lessons learned

Optional: Graph Analysis & Visualization

For participants interested in graph analytics and exploration, you may optionally use tools such as:

• NetworkX (Python)

  • Learning graph algorithms
  • Centrality and community detection
  • Small to medium graphs

• Graph Visualization Tools

  • For understanding and communicating insights
  • Useful for model validation and storytelling

These tools are optional and not required to complete the challenge.

🏆 Prizes & Recognition

We believe learning in public deserves recognition.

Learning & Consistency Prizes

• Awarded to participants who:

• Consistently submit PRs

• Share learnings publicly

• Apply concepts thoughtfully

Prizes include:

🌟 Spotlight on the GraphGeeks Website

🎤 Opportunity to give a GraphGeeks Community Talk

🎁 GraphGeeks SWAG

📘 A graph-related book to continue your journey

🤝 Contribution Guidelines

One PR per day

No “perfect” answers required

Diagrams and explanations encouraged

Be respectful and constructive

Help others learn