Network Graph Analysis

intermediatedataMin 64K context

Analyzes complex networks and graphs using NetworkX, igraph, and PyTorch Geometric. Supports social network analysis, biological interaction networks, knowledge graphs, and citation networks. Performs community detection, centrality analysis, link prediction, graph neural networks, and network visualization with force-directed layouts.

networks graphs networkx community-detection centrality gnn social-networks knowledge-graphs

Use Cases

Analyzing protein-protein interaction networks
Community detection in social networks
Building and querying knowledge graphs
Citation network analysis for research mapping
Identifying influential nodes (centrality measures)
Link prediction for recommendation systems

Example Prompt

Analyze a protein-protein interaction network from STRING database.

Given an edge list of interactions with confidence scores, perform:
1. Load network and compute basic statistics (nodes, edges, density, diameter)
2. Degree distribution analysis (is it scale-free?)
3. Centrality analysis (degree, betweenness, closeness, eigenvector)
4. Community detection (Louvain and Leiden algorithms)
5. Identify hub proteins (top 10 by betweenness centrality)
6. Find network motifs and enriched subgraphs
7. Visualize with communities colored, node size by centrality
8. Export results for Cytoscape

Generate complete Python code using NetworkX and community detection libraries.
Include a summary table of key network metrics.

Recommended Models

claude-opus-4-6 gpt-5 gemini-2-5-pro

Compatible Tools

claude-codecursorkiroany

Modalities

Input: text, code, file

→

Output: text, code

Author

OpenModels Community

@openmodelsrun