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:

Prepare network data in appropriate format
Construct the network from relational data
Analyze key properties (centrality, communities, etc.)
Visualize the network structure
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