network-computation

name	network-computation
description	社会网络计算分析工具，提供网络构建、中心性测量、社区检测、网络可视化等完整的网络分析支持
version	1.0.0
author	socienceAI.com
tags	["network-analysis","social-networks","centrality","community-detection","visualization"]

Network Computation Analysis Skill

Overview

社会网络计算分析技能为社会科学研究提供全面的网络分析支持，包括网络构建、中心性测量、社区检测、网络可视化等，帮助研究者深入理解社会关系结构和动态。

When to Use This Skill

Use this skill when the user requests:

• Social network analysis of relationships
• Network construction from relational data
• Centrality analysis (degree, betweenness, closeness, eigenvector)
• Community detection and clustering
• Network visualization and mapping
• Structural hole analysis
• Brokerage and mediation analysis
• Network density and cohesion measures
• Network evolution and change analysis
• Two-mode network analysis

Quick Start

When a user requests network analysis:

1. Prepare network data in appropriate format
2. Construct the network from relational data
3. Analyze key properties (centrality, communities, etc.)
4. Visualize the network structure
5. Interpret findings in social context

脚本调用时机

当需要执行网络计算分析时，调用 calculate_centrality.py 脚本，该脚本整合了网络分析的主要功能。

输入格式

{
  "network_data": {
    "nodes": [
      {
        "id": "节点唯一ID",
        "label": "节点标签",
        "attributes": {
          "type": "节点类型",
          "size": "节点大小",
          "group": "节点分组"
        }
      }
    ],
    "edges": [
      {
        "source": "源节点ID",
        "target": "目标节点ID",
        "weight": "边权重",
        "type": "边类型",
        "attributes": {
          "strength": "关系强度",
          "direction": "方向性"
        }
      }
    ]
  },
  "analysis_parameters": {
    "network_type": "directed/undirected",
    "is_weighted": true,
    "centrality_metrics": ["degree", "betweenness", "closeness", "eigenvector"],
    "community_method": "louvain/modularity/greedy",
    "visualization_type": "static/interactive",
    "node_attributes": ["type", "size", "group"],
    "edge_attributes": ["weight", "strength", "direction"]
  },
  "analysis_context": "分析背景和目的",
  "research_questions": ["研究问题列表"]
}

输出格式

{
  "summary": {
    "network_size": "网络规模（节点数）",
    "network_density": "网络密度",
    "connected_components": "连通分量数",
    "analysis_time": "分析耗时"
  },
  "details": {
    "network_metrics": {
      "density": "网络密度",
      "clustering_coefficient": "聚类系数",
      "average_path_length": "平均路径长度",
      "diameter": "网络直径",
      "components": {
        "number_of_components": "连通分量数",
        "largest_component_size": "最大连通分量规模"
      }
    },
    "centrality_analysis": {
      "degree_centrality": {
        "node_id": "中心度值"
      },
      "betweenness_centrality": {
        "node_id": "中心度值"
      },
      "closeness_centrality": {
        "node_id": "中心度值"
      },
      "eigenvector_centrality": {
        "node_id": "中心度值"
      },
      "top_nodes_by_centrality": {
        "degree": ["按度中心度排序的节点"],
        "betweenness": ["按中介中心度排序的节点"],
        "closeness": ["按接近中心度排序的节点"],
        "eigenvector": ["按特征向量中心度排序的节点"]
      }
    },
    "community_detection": {
      "number_of_communities": "社区数量",
      "communities": [
        {
          "id": "社区ID",
          "size": "社区规模",
          "nodes": ["节点列表"],
          "modularity": "模块度值",
          "description": "社区描述"
        }
      ],
      "node_to_community": {
        "node_id": "社区ID"
      }
    },
    "structural_analysis": {
      "structural_holes": ["结构洞分析结果"],
      "bridges": ["桥接节点"],
      "brokerage_roles": ["中介角色"]
    }
  },
  "visualization": {
    "static_image": "静态图像链接",
    "interactive_graph": "交互图链接",
    "layout": "布局类型",
    "color_scheme": "配色方案"
  },
  "interpretation": {
    "key_findings": ["关键发现"],
    "social_interpretation": "社会学解释",
    "research_insights": ["研究洞察"]
  }
  },
  "metadata": {
    "timestamp": "时间戳",
    "version": "版本号",
    "skill": "network-computation",
    "analysis_parameters": "分析参数"
  }
}

Core Functions (Progressive Disclosure)

Primary Functions

• Network Construction: Create networks from edgelist or matrix data
• Centrality Analysis: Calculate degree, betweenness, and closeness centrality
• Basic Visualization: Generate network diagrams with standard layouts
• Network Metrics: Compute density, clustering coefficient, path length

Secondary Functions

• Community Detection: Identify clusters using modularity-based methods
• Advanced Centrality: Calculate eigenvector centrality, PageRank
• Structural Analysis: Identify structural holes, components
• Interactive Visualization: Create interactive network diagrams

Advanced Functions

• Multi-layer Networks: Handle networks with multiple relationship types
• Dynamic Networks: Analyze network evolution over time
• Two-mode Networks: Handle bipartite network structures
• Advanced Layouts: Use specialized layout algorithms for large networks

Detailed Instructions

1. Network Data Preparation

• Validate network data format (edgelist, adjacency matrix, etc.)
• Clean and preprocess relational data
• Identify and handle missing or anomalous connections
• Determine appropriate network type (directed/undirected, weighted/unweighted)

2. Network Construction

• Create network object from data
• Validate network properties (connectedness, size, etc.)
• Compute basic network metrics (nodes, edges, density)
• Visualize initial network structure

3. Centrality Analysis

• Calculate multiple centrality measures
• Rank nodes by different centrality indicators
• Identify key players, bridges, and influencers
• Interpret centrality in social context

4. Community Detection

• Apply appropriate clustering algorithms
• Validate community structure
• Analyze inter-community relationships
• Interpret community meaning in social context

5. Network Visualization

• Create clear, informative network diagrams
• Use visual attributes to highlight important features
• Generate publication-quality figures
• Provide interactive visualization when appropriate

6. Interpretation and Reporting

• Explain findings in social science terms
• Connect network properties to research questions
• Consider Chinese social context (guanxi, mianzi, etc.)
• Provide actionable insights

Parameters

• network_format: Input format (edgelist, adjacency matrix, JSON, etc.)
• directed: Whether the network is directed (default: false)
• weighted: Whether the network is weighted (default: true)
• centrality_metrics: List of centrality measures to compute
• community_method: Community detection algorithm to use
• visualization_type: Type of visualization (static, interactive)
• node_attributes: Additional node properties to visualize
• edge_attributes: Additional edge properties to visualize

Examples

Example 1: Centrality Analysis

User: "Analyze this social network and identify the most important actors" Response: Calculate all centrality measures, identify key nodes, interpret in social context.

Example 2: Community Detection

User: "Find communities within this organization's communication network" Response: Apply community detection algorithms, validate structure, interpret meaning.

Example 3: Network Visualization

User: "Create a visualization of this collaboration network" Response: Generate network diagram with appropriate layout, highlight important nodes.

Quality Assurance

• Verify network data integrity before analysis
• Use multiple algorithms when possible for validation
• Consider social context in interpretation
• Ensure visualizations are clear and informative
• Validate community detection results

Output Format

• Complete network analysis report
• Standardized network metrics tables
• Network visualization files (PNG, SVG, interactive HTML)
• Network data files in various formats
• Reproducible analysis code

Resources

• Social network analysis best practices
• Network visualization guidelines
• Community detection algorithm comparisons
• Chinese social network characteristics (guanxi, etc.)
• Python toolkit: skills/network-computation/scripts/calculate_centrality.py

Metadata

• Compatibility: Claude 3.5 Sonnet and above
• Domain: Social Network Analysis
• Language: Optimized for Chinese research context