Network Graph Analysis
中级data最低上下文：64K
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.
使用场景
- 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
示例提示词
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.
推荐模型
兼容工具
claude-codecursorkiroany
模态
输入: text, code, file
→输出: text, code
相关 Skills
作者
OpenModels Community