NetworkX Graph Analysis

关于

This skill equips Claude with specialized expertise in the NetworkX Python library, facilitating the creation, manipulation, and study of complex networks. It provides structured guidance on selecting appropriate graph types—such as Directed and MultiGraphs—implementing centrality measures like PageRank, and executing pathfinding or community detection algorithms. Whether you are building social network models, knowledge graphs, or biological interaction networks, this skill ensures best practices for reproducibility, data exchange with Pandas, and performance optimization for varying graph scales.

主要功能

• Comprehensive guidance for centrality measures including PageRank and Betweenness
• Data I/O optimization for GraphML, JSON, and Pandas DataFrames
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• Built-in graph generators for scale-free and small-world models
• Implementation patterns for shortest path and weighted path algorithms
• Support for community detection methods like Louvain and Label Propagation

使用场景

• Solving logistics and routing problems using weighted shortest path algorithms
• Developing knowledge graphs and ontologies for semantic data modeling
• Analyzing social networks to identify influencers and community clusters