performing-network-computation

Name: performing-network-computation
Availability: InStock
Author: ptreezh

Build social network graphs and compute indicators, centralities, and community structure.

24|7|Updated Nov 15, 2025

Search Tags:#visualization #community detection #social network analysis #graph modeling #network computation #centrality metrics

Ready to run

npx skills add https://github.com/ptreezh/sscisubagent-skills --skill performing-network-computation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: performing-network-computation
Source: https://github.com/ptreezh/sscisubagent-skills/tree/main/archive/skills/analysis/performing-network-computation
Command: npx skills add https://github.com/ptreezh/sscisubagent-skills --skill performing-network-computation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires networkx, numpy, matplotlib, python-louvain, python-louvain-community, and includes scripts (resource) components.

What problem does it solve?

When your social science data is stored as relationships, the task of turning it into a usable network and calculating meaningful statistics (indicators, centrality, and community structure) is time-consuming and error-prone.

Core Features & Use Cases

Network construction: Convert edge lists, adjacency matrices, or survey-style connection records into a network model, supporting directed/undirected and weighted graphs.
Network indicator computation: Calculate network size, density, path-based measures (e.g., diameter/radius/shortest paths), clustering, and connectivity components.
Advanced analytics: Run centrality measures (degree, closeness, betweenness, eigenvector, plus Katz and PageRank/HITS), detect communities (Louvain-style modularity optimization, label propagation, hierarchical/spectral-style approaches), and produce visualization outputs for reporting.

Quick Start

Use the performing-network-computation skill to compute indicators, centralities, and community structure for your social network dataset provided as an edge list.

Frequently Asked Questions about performing-network-computation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I compute centrality metrics and detect communities from an edge list?▼

To compute centrality metrics and detect communities from an edge list, use this Skill to build a network graph and execute algorithms like Louvain modularity optimization, label propagation, and PageRank. It outputs tabular metrics and visualization artifacts.

What network indicators can I calculate for social network analysis?▼

For social network analysis, you can calculate network size, density, diameter, radius, shortest paths, clustering coefficients, and connectivity components. The Skill also computes degree, closeness, betweenness, and eigenvector centralities.

Does this Skill support directed and weighted graph modeling?▼

Yes, this Skill supports directed and weighted graph modeling. It converts edge lists, adjacency matrices, or survey-style connection records into network models, allowing parameter selection for directed or weighted handling during graph construction.

Can I use NetworkX and python-louvain for community detection in my research workflow?▼

Yes, you can use NetworkX and python-louvain for community detection. The Skill leverages these dependencies to apply Louvain-style modularity optimization, label propagation, and hierarchical or spectral approaches to generate visualization-ready outputs.

What's the best way to visualize graph analytics outputs for reporting?▼

The best way to visualize graph analytics outputs for reporting is to use the Skill's built-in matplotlib generation. It produces visualization artifacts directly from the computed network metrics and community structures for immediate use in reports.

What are the limitations of using Louvain modularity for community detection?▼

While the Skill executes Louvain modularity optimization deterministically, limitations of this approach include potential resolution limits where smaller communities might be merged, which is why label propagation and spectral methods are also offered as alternatives.