What is a second-order network in this context?

A second-order network analyzes connections beyond direct followers, looking at 'friends-of-friends' to provide a deeper view of potential reach and community structure.

Does this skill support data export?

Yes, the skill utilizes a standardized DuckDB schema for nodes, edges, and adoption events, allowing for easy querying and integration with other data tools.

How does it track idea adoption?

It uses fingerprinting models to identify specific topics or ideas and traces their spread from an origin user through an interaction graph over a set time window.

What is the reach multiplier metric?

The reach multiplier is calculated by dividing second-order reach by direct reach, quantifying the amplification factor of a specific user's influence.

Influence Propagation & Network Analysis

Name: Influence Propagation & Network Analysis
Author: plurigrid

byplurigrid

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Analíticas y Monitorización

Analyzes and traces the flow of ideas, topics, and influence across complex social and technical networks using interperspectival metrics.

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This skill provides a specialized framework for Layer 7 network analysis, enabling Claude to map how ideas and behaviors propagate through communities. It extends standard network analysis with second-order depth and idea adoption tracking to measure reach multipliers and identify key amplifiers within a system. By integrating interperspectival mapping, it allows users to understand not just connectivity, but how consensus forms and how different participants perceive influence flows, making it ideal for community researchers and data scientists.

Características Principales

Map topic propagation using Directed Acyclic Graphs (DAGs)
Analyze second-order connections to measure friends-of-friends reach
Generate interperspectival maps to understand participant perceptions and roles
Trace idea adoption timelines and identify key network amplifiers
2 GitHub stars
Calculate reach multipliers to quantify influence amplification factors

Casos de Uso

Identifying influential nodes and thought leaders in open-source communities
Analyzing community sentiment and perspective shifts over specific time windows
Modeling the spread of technical concepts or documentation updates across a network

Acerca de

Características Principales

Map topic propagation using Directed Acyclic Graphs (DAGs)
Analyze second-order connections to measure friends-of-friends reach
Generate interperspectival maps to understand participant perceptions and roles
Trace idea adoption timelines and identify key network amplifiers
2 GitHub stars
Calculate reach multipliers to quantify influence amplification factors

Casos de Uso

Identifying influential nodes and thought leaders in open-source communities
Analyzing community sentiment and perspective shifts over specific time windows
Modeling the spread of technical concepts or documentation updates across a network