How does this skill use cellular sheaves?

It uses cellular sheaves to model local interaction data and analyze diffusion patterns via Laplacians, allowing for a more nuanced view of how influence flows across different network layers.

What databases are supported for network storage?

The skill includes a pre-defined schema for DuckDB, allowing for high-performance SQL queries on network nodes, edges, and adoption timelines.

Can I integrate this with live data sources?

Yes, it is designed to work with the GF(3) triad, including pulse-mcp-stream for live data ingestion and bisimulation-game for pattern verification.

What specific metrics can I calculate?

You can measure reach multipliers (amplification factors), adoption rates (percentage of network uptake), and decay half-life (how long an idea persists).

What is influence propagation in this context?

It is the mathematical study of how information, ideas, or behaviors spread through a social or digital network from source nodes to the wider population.

Influence Propagation Analysis

Name: Influence Propagation Analysis
Author: plurigrid

byplurigrid

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Data Science & ML

Analyzes and traces the flow of ideas, topics, and behaviors across complex social and interperspectival networks.

About

The Influence Propagation skill provides advanced tools for Layer 7 network analysis, utilizing cellular sheaves and condensed stacks to model how influence spreads through digital ecosystems. By integrating DuckDB for efficient interaction tracking and Hy for perspective mapping, it enables developers to calculate reach multipliers, adoption rates, and identify key amplifier nodes. This skill is ideal for projects requiring deep sociological modeling, viral content tracking, or topological data analysis of complex user interactions.

Key Features

Models diffusion using cellular sheaves and Laplacian-based flow analysis
2 GitHub stars
Identifies key network amplifiers and bridge nodes using betweenness metrics
Calculates reach multipliers and second-order network adoption rates
Maps interperspectival threads through automated perspective mapping
Integrates with DuckDB for high-performance interaction and edge tracking

Use Cases

Identifying influential nodes and bridge-builders within a professional community
Tracking the viral spread of specific concepts or hashtags across social platforms
Simulating the persistence and decay of ideas over time using topological math

About

Key Features

Models diffusion using cellular sheaves and Laplacian-based flow analysis
2 GitHub stars
Identifies key network amplifiers and bridge nodes using betweenness metrics
Calculates reach multipliers and second-order network adoption rates
Maps interperspectival threads through automated perspective mapping
Integrates with DuckDB for high-performance interaction and edge tracking

Use Cases

Identifying influential nodes and bridge-builders within a professional community
Tracking the viral spread of specific concepts or hashtags across social platforms
Simulating the persistence and decay of ideas over time using topological math