# PapersGraph

> PapersGraph is an academic research platform for discovering, searching, and visualizing AI and machine learning research papers through interactive citation networks. It provides graph-based exploration of 10,000+ research papers, datasets, and state-of-the-art benchmarks across artificial intelligence, machine learning, computer vision, and natural language processing.

PapersGraph helps researchers, students, and professionals navigate the connected world of academic research. Each paper links to its citations and references, forming an interactive visual graph powered by D3.js force-directed layouts. Users can explore trending papers, filter by field of study, and discover related work through citation pathways.

## Core Features

- **Interactive Citation Graphs**: Visualize how research papers connect through citations and references using force-directed graph layouts
- **Research Paper Search**: Full-text search across 10,000+ papers by title, authors, DOI, or keywords
- **Trending Papers**: Curated feed of the most cited and impactful recent AI research
- **Datasets Directory**: Browse ML/AI datasets with details on tasks, modalities, variants, loaders, and linked papers
- **State-of-the-Arts**: Track benchmark leaderboards and model performance across research tasks
- **Paper Details**: Full paper metadata including abstract, authors, venue, citations count, references, TLDR, and PDF links

## Main Sections

- [Home](https://papersgraph.com): Trending research papers feed with search and discovery
- [Research Papers](https://papersgraph.com/research-papers): Browse and search the full catalog of research papers
- [Datasets](https://papersgraph.com/datasets): Explore ML/AI datasets with tasks, modalities, and linked papers
- [State-of-the-Arts](https://papersgraph.com/state-of-the-arts): Benchmark leaderboards and performance comparisons

## Key Pages

- [Research Papers Search](https://papersgraph.com/research-papers/search): Search papers by title, keyword, author, or DOI
- [About](https://papersgraph.com/about): Platform overview and team information
- [Pricing](https://papersgraph.com/pricing): Subscription plans for accessing advanced features
- [Contact](https://papersgraph.com/contact): Get in touch with the PapersGraph team
- [Sign In](https://papersgraph.com/auth/signin): User authentication
- [Sign Up](https://papersgraph.com/auth/signup): Create a new account

## Content Types

### Research Papers
Each research paper page includes:
- Title, abstract, authors, venue, and publication year
- TLDR (one-sentence summary)
- Citation and reference counts
- Fields of study / research areas
- PDF download link
- Interactive citation network graph
- Related papers

### Datasets
Each dataset page includes:
- Name, description, and full name
- Modalities (image, text, audio, video, etc.)
- Languages supported
- Associated tasks and benchmarks
- Dataset variants
- Data loaders (PyTorch, TensorFlow, HuggingFace, etc.)
- License information
- Linked research papers that use the dataset
- Similar datasets

### Citation Graphs
Each graph visualization includes:
- Force-directed node-link diagram
- Nodes representing individual papers
- Edges representing citation relationships
- Zoom, pan, and node selection controls
- Paper details panel on node selection

## Technical Details

- Built with Next.js (App Router), React, TypeScript, Tailwind CSS
- Graph visualization powered by D3.js
- Academic data sourced from Semantic Scholar and similar databases
- API-driven dynamic content

## Sitemap

- [Sitemap](https://papersgraph.com/sitemap.xml)
- [Robots](https://papersgraph.com/robots.txt)