Network Topology Metric Computation

Reconstruct topological and stability metric computation for a constructed network

Summary

Compute comprehensive topological and stability metrics from a network object to characterize global and node-level network properties. MetaNet's metrics module calculates degree, centrality, clustering coefficients, path lengths, robustness, and perturbation-response metrics for in-depth network characterization.

When to use

After constructing or loading a network object (from adjacency matrix, edge list, or correlation output) and needing to quantify structural properties—such as identifying hub nodes via degree centrality, assessing clustering via coefficient distributions, measuring network resilience via robustness scores, or benchmarking networks across conditions or datasets.

When NOT to use

• Network object is unavailable or not yet constructed; construct network first using c_net_build() or multi_net_build().
• Goal is visualization or layout only; use c_net_plot() or layout functions instead.
• Raw correlation or abundance data requires filtering or normalization prior to network construction; preprocess and construct network before invoking metrics.

Inputs

• network object (igraph or metanet class)
• adjacency matrix
• edge list
• correlation matrix output

Outputs

• topological metrics table (nodes × metrics)
• network-level metrics summary
• robustness assessment report
• perturbation-response matrix
• delimited or Excel export of metrics

How to apply

Load or construct a network object in MetaNet format. Invoke MetaNet's metrics module to compute topological metrics—degree, centrality measures (betweenness, closeness, eigenvector), clustering coefficient, and path length statistics. Then invoke stability assessment functions to compute network robustness (e.g., response to node/edge removal) and perturbation-response metrics. Aggregate all computed metrics into a structured table with rows representing nodes or network-level properties and columns as metric names and values. Export the metrics table as a delimited (CSV/TSV) or Excel file for downstream statistical analysis or visualization.

Related tools

• MetaNet (Primary package providing metrics module and stability assessment functions for topological and robustness computation) — https://github.com/Asa12138/MetaNet
• igraph (Underlying graph library on which MetaNet's core functionality is built; provides graph algorithms and centrality measures)
• pcutils (Companion utility package for data manipulation and preprocessing prior to or following metric computation) — https://github.com/Asa12138/pcutils
• R (Runtime environment for executing MetaNet metrics and aggregation workflows)

Examples

# After constructing network in MetaNet:
multi1 <- multi_net_build(list(Microbiome = micro, Metabolome = metab, Transcriptome = transc), r_threshold = 0.6)
# Compute topological metrics (implicit in MetaNet object; explicit extraction and export would require custom aggregation via igraph functions and write.csv())

Evaluation signals

• Metrics table is non-empty with all expected columns (degree, centrality variants, clustering coefficient, path length, robustness scores) and rows matching node count or network properties.
• All computed metric values fall within plausible ranges (e.g., degree ≤ network size, clustering coefficient ∈ [0, 1], centrality measures normalized appropriately).
• No missing or infinite values in output; check for NaN or Inf entries that indicate computation failure or numerical instability.
• Robustness metrics show monotonic or expected decay pattern under progressive perturbation (node/edge removal scenarios).
• Export file is valid and parseable in downstream tools (CSV schema validation, no truncation, correct delimiters and encoding).

Limitations

• Computation scales with network size; MetaNet optimizes for datasets with >10,000 features but performance depends on density and system memory.
• Stability metrics (robustness, perturbation-response) require multiple simulation runs; results may vary slightly across runs unless seed is fixed.
• Directed vs. undirected networks and weighted vs. unweighted edges require appropriate metric selection; some centrality measures have different meanings for directed graphs.
• Isolated nodes or disconnected subgraphs may produce undefined path length or centrality values; filtering or handling of such nodes should be documented.
• Multi-omics networks require care when interpreting metrics across layers; intra-omics vs. inter-omics edges may need separate or stratified metric computation.

Evidence

• [other] MetaNet provides comprehensive topological and stability metrics as part of its network analysis capabilities for in-depth network characterization.: "MetaNet provides comprehensive topological and stability metrics as part of its network analysis capabilities for in-depth network characterization."
• [other] Invoke MetaNet's metrics module to compute comprehensive topological metrics (e.g., degree, centrality, clustering coefficient, path length) and stability assessment functions to compute network robustness and perturbation-response metrics.: "Invoke MetaNet's metrics module to compute comprehensive topological metrics (e.g., degree, centrality, clustering coefficient, path length). 3. Invoke MetaNet's stability assessment functions to"
• [intro] It further offers comprehensive topological and stability metrics for in-depth network characterization.: "It further offers comprehensive topological and stability metrics for in-depth network characterization."
• [readme] MetaNet's architecture comprises several core functional modules: Calculation, Manipulation, Layout, Visualization, Topology analysis, Module analysis, Stability analysis, and I/O, supporting the end-to-end analytical process from network construction to visualization.: "its architecture comprises several core functional modules: Calculation, Manipulation, Layout, Visualization, Topology analysis, Module analysis, Stability analysis, and I/O"
• [other] Aggregate all computed metrics into a structured table (rows = nodes or network properties; columns = metric names and values) and export the metrics table as a delimited or Excel file for downstream analysis or visualization.: "Aggregate all computed metrics into a structured table (rows = nodes or network properties; columns = metric names and values). 5. Export the metrics table as a delimited or Excel file for downstream"