CitationFlow.AI

Graph-Based Visualization of the AI Research Landscape

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📌 Table of Contents

• 🚀 Overview
• 🛠️ Tech Stack
• ⚡ Getting Started
• 🗺️ Roadmap
• 🔮 Upcoming Features
• 🙏 Acknowledgements
• 👥 Contributors
• 📜 License

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🚀 Overview

CitationFlow.AI is an interactive tool that maps the evolving landscape of artificial intelligence research as a dynamic citation graph. It enables users to visually explore relationships between papers, discover emerging topics, and identify influential authors—without reading hundreds of articles.

Try by your self here

🔑 Key Features

Feature	Description
Graph Visualization	Force-directed graph of articles (nodes) and citations (edges).
Insightful Statistics	Explore top authors, trending topics, and dataset summaries.
Advanced Filtering	Drill down into specific research areas, timeframes, or authors.

Target Users: AI researchers, students, and professionals exploring scholarly literature.
Data Source: ScienceDirect API

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🛠️ Tech Stack

🗃️ Backend & Database

Technology	Purpose
Python 3.9+	Data pipeline, API integration, backend logic
FastAPI	Fast and modern web API framework
Pandas	Data wrangling and transformation
PostgreSQL	Structured storage for citation networks

🎨 Frontend & Visualization

Technology	Purpose
D3.js 7.8+	Graph rendering and force simulations
JavaScript (ES6+)	Interactive UI and event handling

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⚡ Getting Started

🐳 Quick Setup (via Docker)

1. Create a .env file with your database credentials:

   DB_USER=postgres
   DB_PASSWORD=password
   DB_NAME=cf_test
   DB_HOST=db
   DB_PORT=5432

2. Clone the repository:

   git clone https://github.com/Data-Wrangling-and-Visualisation/CitationFlow.AI
   cd CitationFlow.AI

3. Build and launch the app:

   docker-compose up --build

4. Access the app in your browser:
   🔗 http://localhost

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🗺️ Roadmap

Phase	Status	Key Tasks
Project Setup & API Integration	✅	Base structure, ScienceDirect API integration
Data Pipeline	✅	Extraction, cleaning, DB schema design
Visualization	✅	Interactive D3.js graph
UI/UX Enhancements	✅	Filters, search, responsive interface
Performance Optimization	✅	Graph rendering optimizations and testing

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🔮 Upcoming Features

• 🕒 Temporal Visualization – View research trends evolving over time
• 🎛️ Custom Views – Save/export subgraphs and favorite topics
• 🤝 Collaboration Tools – Shared bookmarks, notes, and annotations

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🙏 Acknowledgements

• 📚 Data powered by ScienceDirect (Elsevier)
• 🎓 Developed with support from Innopolis University

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👥 Contributors

Role	Name	Email
Team Lead	Marsel Berheev	m.berheeev@innopolis.university
Data Engineer	Nikita Stepankov	n.stepankov@innopolis.university
DB Architect	Makar Egorov	m.egorov@innopolis.university

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📜 License

This project is licensed under the MIT License.

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✨ Explore the future of AI research with CitationFlow.AI ✨