# Network Graph Serialization

> Use when you have applied graphical lasso (GLASSO) to estimate a sparse inverse covariance matrix from a feature matrix and need to convert the non-zero precision matrix entries into an explicit graph representation suitable for visualization, topology analysis, or overlay with PCA scores.

- Skill: `holobiomicslab/network-graph-serialization` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/network-graph-serialization`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/network-graph-serialization/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/network-graph-serialization

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# network-graph-serialization

> **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->
## Summary

Extract and serialize a network graph structure from a sparse precision matrix (produced by GLASSO) into a portable format (edge list, adjacency matrix, or GraphML). This enables downstream visualization, analysis, and integration with PCA-based feature scoring.

## When to use

You have applied graphical lasso (GLASSO) to estimate a sparse inverse covariance matrix from a feature matrix and need to convert the non-zero precision matrix entries into an explicit graph representation suitable for visualization, topology analysis, or overlay with PCA scores.

## When NOT to use

- The precision matrix is dense or fully connected (no sparsity to exploit).
- You need to perform additional statistical filtering on edge weights before serialization (apply such filtering to the precision matrix first).
- Your downstream analysis tool expects a different input format (e.g., raw feature matrix) and does not accept graph representations.

## Inputs

- Sparse inverse covariance (precision) matrix from GLASSO
- Feature names / node labels (from original feature matrix columns)

## Outputs

- Edge list (tab- or comma-delimited)
- Adjacency matrix (dense or sparse)
- GraphML file
- Network graph object (nodes, edges, weights)

## How to apply

After GLASSO estimation produces a sparse precision matrix with sparsity controlled by a regularization parameter (lambda), identify all non-zero entries as candidate edges between features. Construct a graph representation by mapping features to nodes and non-zero precision matrix entries to weighted edges, preserving the precision values as edge weights. Serialize the resulting node and edge sets into a structured output format such as an edge list (tab/comma-delimited pairs with weights), an adjacency matrix, or GraphML; the choice depends on downstream tool compatibility and whether edge weights or node metadata must be preserved. Validate the serialization by spot-checking that the number and direction of edges match the sparsity pattern of the original precision matrix.

## Related tools

- **pcaGLASSO** (Generates sparse precision matrix via GLASSO; graph serialization converts this matrix into portable network format for PCA score overlay and sub-network identification.) — https://github.com/jlichtarge/pcaGLASSO

## Evaluation signals

- Edge count in serialized output matches the number of non-zero off-diagonal entries in the precision matrix.
- All node labels (features) are present and correctly mapped in the output format.
- Edge weights (precision values) are preserved and fall within the expected range from the original matrix.
- Serialized graph can be re-imported into a network analysis or visualization tool without parsing errors.
- Symmetry of edges (undirected graph) is consistent with the precision matrix structure.

## Limitations

- No changelog available in the pcaGLASSO package, limiting version-to-version format compatibility tracking.
- Serialization format choice (edge list vs. adjacency matrix vs. GraphML) affects file size and downstream tool compatibility; no single format is universally optimal.
- The precision matrix sparsity pattern and edge weight magnitudes depend entirely on the GLASSO regularization parameter (lambda); suboptimal lambda choice will produce a graph that does not reflect true network structure.

## Evidence

- [other] Extract the network graph structure from the precision matrix by identifying non-zero entries as edges between features.: "Extract the network graph structure from the precision matrix by identifying non-zero entries as edges between features."
- [other] Serialize the network graph (nodes, edges, weights) to a structured output format (e.g., edge list, adjacency matrix, or GraphML).: "Serialize the network graph (nodes, edges, weights) to a structured output format (e.g., edge list, adjacency matrix, or GraphML)."
- [readme] Graphs generated by GLASSO are overlaid with PCA scores to identify significant sub-networks.: "Graphs generated by GLASSO are overlaid with PCA scores to identify significant sub-networks."