processing-network-data

What problem does it solve?

This Skill turns messy raw relationship data from surveys, interviews, observations, or digital records into standardized social network analysis inputs like node lists, edge lists, adjacency matrices, and data-quality reports.

Core Features & Use Cases

• Relationship data extraction: Identify relationships and (optionally) weights from multiple data sources (questionnaires, interview text, observation coding, digital interaction logs).
• Network matrix construction: Build node sets and construct adjacency matrices, supporting undirected/weighted/multi-attribute patterns.
• Data cleaning, validation, and quality reporting: Detect missing values, inconsistencies, and anomalies, then output structured results with a quality assessment record.
• Attribute integration for nodes and edges: Merge node attributes, encode edge attributes (strength/frequency), and handle time-related relationship information.

Use Case: If you have raw survey exports listing collaborators and relationship frequencies, use this Skill to convert them into an edge list and adjacency matrix suitable for NetworkX/igraph analysis.

Quick Start

Ask the skill to process your raw relationship dataset and output a standardized edge list, adjacency matrix, node/edge attributes, and a data quality report.