A Curated Collection of Graph Use Cases and Datasets
Welcome to Graph Explorer, a community-driven project designed to showcase a comprehensive range of graph use cases and datasets. Whether you're just starting with graphs or you're a seasoned professional, this resource aims to make exploring graph technology easier and more intuitive.
Our focus is to provide you with real-world examples of how graphs are being applied across industries, alongside datasets that can help you start your own graph-powered projects.
Graphs are revolutionizing industries by uncovering hidden relationships and patterns within data. Below is a curated list of use cases where graph technology has demonstrated real impact:
Fraud detection in industries like finance and e-commerce benefits from graphs by linking various entities—transactions, customers, and merchants—into a network, uncovering fraudulent patterns.
Example Use Cases:
- Transaction Networks: Detect suspicious patterns in financial transaction networks by linking accounts with abnormal behavior.
- Credit Card Fraud: Identify clusters of fraudulent activities through relationships between credit card transactions and merchant data.
Real-World Example:
- PayPal: Uses graph algorithms to identify complex fraud networks, connecting otherwise unrelated transactions to detect fraud.
Graphs help analyze and understand social interactions, influence, and relationships in social media platforms, marketing, and online communities.
Example Use Cases:
- Influencer Detection: Identify key individuals in social media who influence the spread of information.
- Community Detection: Detect and analyze communities within large social networks to better understand social dynamics.
Real-World Example:
- Facebook: Uses graph technology to map out connections between users, enabling recommendations for friends and ads based on network analysis.
Graphs can optimize supply chains by providing a clear picture of the relationships between suppliers, manufacturers, logistics partners, and retailers.
Example Use Cases:
- Logistics Optimization: Use graphs to find optimal routes for delivery networks, minimizing transportation costs and time.
- Risk Management: Track relationships across supply chain entities to identify vulnerabilities and risks, such as dependency on single suppliers.
Real-World Example:
- Walmart: Uses graph technology to manage its complex supply chain, analyzing millions of relationships between products, suppliers, and stores.
Recommendation engines utilize graphs to make personalized recommendations by exploring the relationships between users, products, and interactions.
Example Use Cases:
- Product Recommendations: Analyze purchase histories and product similarities to recommend new items to customers.
- Content Recommendations: Use graphs to connect users with content (movies, articles, etc.) based on shared preferences or behaviors.
Real-World Example:
- Amazon: Uses co-purchasing networks and graph algorithms to recommend products based on users' purchasing history and preferences.
Graphs are used to study complex biological systems by modeling interactions between proteins, genes, and other biological entities.
Example Use Cases:
- Gene Regulation Networks: Analyze how genes influence each other to understand biological processes and diseases.
- Protein-Protein Interactions: Explore relationships between proteins to discover potential drug targets.
Real-World Example:
- Human Genome Project: Uses graph-based analysis to map out relationships between genes and proteins, leading to advances in medical research.
To help you get started with your own graph-based projects, here’s a collection of publicly available datasets. These datasets are ideal for experimenting with graph algorithms and exploring new use cases.
A collection of emails from the Enron Corporation, ideal for studying communication networks and detecting fraud patterns.
Link: Enron Dataset
This dataset provides information on product co-purchasing networks, useful for building recommendation systems.
Link: Amazon Co-purchase Dataset
A dataset of scientific publications related to COVID-19, ideal for biomedical graph analysis, such as analyzing research trends and relationships between topics.
Link: CORD-19 Dataset
Data on Facebook user connections and circles, useful for social network analysis and community detection.
Link: Facebook Social Circles Dataset
A dataset on global trade between countries, ideal for analyzing trade flows, economic relationships, and supply chain management.
Link: World Trade Network Dataset
Whether you're analyzing a complex supply chain or trying to detect fraud in a massive transaction network, Graph Explorer is your go-to resource for real-world graph use cases and the datasets to support them. Dive into the world of graphs and unlock the hidden potential of your data!
Contributions are welcome! If you know of any graph usecases and datasets, that should be included, feel free to submit a pull request or open an issue.
Please follow the contribution guidelines to maintain the quality and consistency of graph explorer.
This repository is licensed under CC0 1.0 Universal, which means you can freely use and contribute to this project.
Happy Graph Exploring! 😊