Networkx MCP Server

Academic-focused graph analysis in your AI conversations - The first and only NetworkX MCP server specialized for academic research and citation analysis.

🎓 What is this?

NetworkX MCP Server enables Large Language Models (like Claude) to perform sophisticated academic research and citation analysis directly within conversations. Built specifically for researchers, academics, and students who need to analyze citation networks, calculate author impact metrics, and discover literature patterns.

Stop switching between VOSviewer, CitNetExplorer, and manual analysis. Start doing academic research in your AI conversations.

🎯 Key Features

🔬 Academic Research Tools

Citation Network Analysis: Build citation networks from DOIs using CrossRef API
Author Impact Metrics: Calculate h-index, total citations, and academic influence
Literature Discovery: Automated paper recommendations based on citation patterns
Collaboration Analysis: Map co-authorship networks and identify key researchers
Research Trend Detection: Analyze publication and citation trends over time

📊 Core Graph Operations

20+ Graph Functions: From basic operations to advanced algorithms like PageRank
BibTeX Export: Export citation networks in academic-standard BibTeX format
CrossRef Integration: Access 156+ million academic papers via DOI resolution
Visualization: Generate publication-ready network visualizations
First of Its Kind: The only academic-focused NetworkX MCP server

🌟 Why NetworkX MCP Server for Academic Research?

Built for Researchers: Designed specifically for academic workflows and citation analysis
Real-time Literature Discovery: Find related papers and collaboration opportunities instantly
Reproducible Research: Python-based, version-controlled, and shareable analysis workflows
Academic Data Integration: Direct access to CrossRef's 156+ million paper database
No Enterprise Complexity: Focus on research, not IT infrastructure
Cost-Effective: Free alternative to expensive commercial citation analysis tools

📦 Installation

pip install networkx-mcp-server

🚀 Setup

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "networkx": {
      "command": "python",
      "args": ["-m", "networkx_mcp.server"]
    }
  }
}

📊 Available Operations

🔬 Academic Research Functions

resolve_doi - Resolve DOI to publication metadata using CrossRef API
build_citation_network - Build citation networks from seed DOIs
analyze_author_impact - Calculate h-index and impact metrics for authors
find_collaboration_patterns - Analyze co-authorship networks
detect_research_trends - Identify publication and citation trends over time
recommend_papers - Get paper recommendations based on citation patterns
export_bibtex - Export citation networks in BibTeX format

📊 Core Graph Operations

create_graph - Create directed or undirected graphs
add_nodes - Add nodes to your graph
add_edges - Connect nodes with edges
get_info - Get basic graph statistics
shortest_path - Find optimal paths between nodes

🔍 Analysis Operations

degree_centrality - Find the most connected nodes
betweenness_centrality - Identify bridges and key connectors
pagerank - Google's PageRank algorithm for node importance
connected_components - Find isolated subgraphs
community_detection - Discover natural groupings

🎨 Visualization & I/O

visualize_graph - Create PNG visualizations with multiple layouts
import_csv - Load graphs from edge lists
export_json - Export graphs in standard formats

🚦 Quick Start

Community Edition

# Install community edition
pip install networkx-mcp-server

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "networkx": {
      "command": "networkx-mcp",
      "args": []
    }
  }
}

Academic Research Example

Human: Analyze citation patterns for the paper "Attention Is All You Need"

Claude: I'll help you analyze citation patterns for that influential paper.

[Resolves DOI: 10.5555/3295222.3295349]
Found paper: "Attention Is All You Need" by Vaswani et al. (2017)
Citations: 82,892 | Journal: NIPS

[Builds citation network from seed DOI]
Built citation network with 847 nodes and 2,341 edges from 2-hop analysis

[Analyzes author impact]
Ashish Vaswani: h-index 45, total citations 127,436
Most impactful paper: "Attention Is All You Need" (82,892 citations)

[Finds collaboration patterns]
Key collaborators: Noam Shazeer (Google), Niki Parmar (Google)
Research cluster: Google Brain team with 47 collaborations

[Detects research trends]
Trend: MASSIVE INCREASE in attention mechanism research post-2017
2017: 12 papers → 2023: 3,847 papers (320x growth)

[Recommends related papers]
Top recommendations based on co-citation patterns:
1. "BERT: Pre-training of Deep Bidirectional Transformers" (2018)
2. "GPT-2: Language Models are Unsupervised Multitask Learners" (2019)
3. "RoBERTa: A Robustly Optimized BERT Pretraining Approach" (2019)

[Exports BibTeX]
Generated BibTeX file with 847 entries ready for LaTeX integration

🎓 Academic Use Cases

1. Literature Review & Meta-Analysis

Automatically expand citation networks from key papers
Identify research gaps and emerging trends
Calculate field-wide impact metrics
Generate comprehensive BibTeX databases

2. Collaboration Network Analysis

Map research collaborations within and across institutions
Identify key researchers and potential collaborators
Analyze interdisciplinary connections
Study research community evolution

3. Citation Pattern Analysis

Track knowledge diffusion through citation networks
Identify influential papers and breakthrough research
Analyze citation bias and self-citation patterns
Study geographic and institutional citation patterns

4. Research Trend Detection

Identify emerging research areas and hot topics
Analyze publication volume and citation trends
Track research lifecycle from emergence to maturity
Predict future research directions

5. Academic Impact Assessment

Calculate comprehensive author impact metrics
Compare researchers across different career stages
Analyze journal and conference impact patterns
Study citation half-life and research longevity

See the demos/ folder for complete examples.

📈 Performance

Memory: ~70MB (including Python, NetworkX, and visualization)
Graph Size: Tested up to 10,000 nodes
Operations: Most complete in milliseconds
Visualization: 1-2 seconds for complex graphs

🛠️ Development

Running from Source

# Clone the repository
git clone https://github.com/brightlikethelight/networkx-mcp-server
cd networkx-mcp-server

# Install dependencies
pip install -e .

# Run the server
python -m networkx_mcp.server_minimal

Running Tests

pytest tests/working/

📚 Documentation

API Reference - Detailed operation descriptions
Examples - Real-world use cases
Contributing - How to contribute

🤝 Contributing

We welcome contributions! This is the first NetworkX MCP server, and there's lots of room for improvement:

Add more graph algorithms
Improve visualization options
Add graph file format support
Optimize performance
Write more examples

📄 License

MIT License - See LICENSE for details.

🙏 Acknowledgments

NetworkX - The amazing graph library that powers this server
Anthropic - For creating the Model Context Protocol
The MCP community - For inspiration and examples

Built with ❤️ for the AI and Graph Analysis communities