network-computation

To compute centrality and detect communities from an edge list, this Skill builds a graph network and measures key nodes using degree, betweenness, closeness, and PageRank metrics while applying Louvain or Girvan-Newman algorithms to identify community structure.

Structural hole analysis in social networks identifies gaps between communities where actors bridge disconnected groups, and this Skill computes advanced diagnostics to measure these structural holes alongside small-world metrics and robustness checks for comprehensive network analysis.

Yes, you can visualize community boundaries and color nodes in a network graph because this Skill generates clear network visualizations with community coloring and supports dynamic network views for reporting and publication-ready outputs.

Network analysis works with weighted and directed graphs because this Skill explicitly supports directed, undirected, weighted, and unweighted networks for computing centrality, community detection, and advanced structural metrics.

The best way to identify influential actors in an organization using social network analysis is to compute multiple centrality measures simultaneously, which this Skill does by calculating degree, betweenness, closeness, and PageRank metrics from relational inputs.

To calculate network density and degree distributions from an adjacency matrix, this Skill constructs the network and computes core statistics including density, connectivity, and degree distributions as baseline diagnostics before running advanced community detection algorithms.