performing-network-computation

To compute centrality metrics and detect communities from an edge list, use this Skill to build a network graph and execute algorithms like Louvain modularity optimization, label propagation, and PageRank. It outputs tabular metrics and visualization artifacts.

For social network analysis, you can calculate network size, density, diameter, radius, shortest paths, clustering coefficients, and connectivity components. The Skill also computes degree, closeness, betweenness, and eigenvector centralities.

Yes, this Skill supports directed and weighted graph modeling. It converts edge lists, adjacency matrices, or survey-style connection records into network models, allowing parameter selection for directed or weighted handling during graph construction.

Yes, you can use NetworkX and python-louvain for community detection. The Skill leverages these dependencies to apply Louvain-style modularity optimization, label propagation, and hierarchical or spectral approaches to generate visualization-ready outputs.

The best way to visualize graph analytics outputs for reporting is to use the Skill's built-in matplotlib generation. It produces visualization artifacts directly from the computed network metrics and community structures for immediate use in reports.

While the Skill executes Louvain modularity optimization deterministically, limitations of this approach include potential resolution limits where smaller communities might be merged, which is why label propagation and spectral methods are also offered as alternatives.