network-computation-expert

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

Transform social relationship data into networks and compute centrality and community detection results.

24|7|Updated Nov 15, 2025

Search Tags:#visualization #data processing #network analysis #research methodology #centrality #community detection #social networks

Ready to run

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

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

It helps you convert raw social-science relationship data into a structured network, then compute key network indicators, discover communities, and produce analysis-ready results for interpretation.

Core Features & Use Cases

Network data processing: Extract relationship records from different source types, clean/validate them, and standardize the network input.
Network construction: Build standardized node/edge representations (directed/undirected, weighted/unweighted) and attach node/edge attributes.
Network analytics & outputs: Compute centrality measures, detect communities, and summarize network structure with publishable-quality results and visual/graph-ready data.

Quick Start

Use the network-computation-expert skill to process your JSON relationship data, construct the network, compute centralities, run community detection, and return an integrated analysis summary.

Frequently Asked Questions about network-computation-expert

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

FAQPage Schema

How do I compute centrality and community detection from raw social network data?▼

To compute centrality and community detection, you can process structured JSON relationship data to construct a network, calculate centrality measures, and run community detection algorithms for an integrated analysis summary.

What is the best way to analyze social network relationships from survey and interview datasets?▼

Analyzing social network relationships from survey or interview datasets involves extracting structured nodes and edges, standardizing the input, and computing network analytics to produce consolidated, analysis-ready results.

Does this network analysis workflow support directed and weighted relationship data?▼

Yes, the network analysis workflow supports directed and weighted relationship data, allowing you to build standardized node and edge representations while attaching specific node and edge attributes for computation.

Can I use this for processing digital-trace relationship datasets in social science studies?▼

Yes, you can use this for digital-trace relationship datasets, as the workflow applies to end-to-end network analysis for survey, interview, observation, or digital-trace data in social science studies.

What format does the network analysis output use for centrality and community detection results?▼

The network analysis output uses a consolidated JSON report format, delivering centrality measures, community detection summaries, and graph-ready data for interpretation and visualization.