network-analysis-math

To compute centrality metrics, you can load your network data from an edge list or pandas DataFrame into networkx, then calculate degree, betweenness, closeness, PageRank, and eigenvector centrality to identify influential nodes.

Yes, you can perform community detection using the Leiden algorithm by combining python-igraph with the leidenalg library. This approach optimizes modularity to identify and label node clusters within your network.

You can compare real networks against random graph models by generating Erdős–Rényi, Barabási–Albert, and Watts–Strogatz graphs. This helps evaluate network properties like density, clustering, and path lengths against theoretical baselines.

Python network analysis requires networkx for graph construction, python-igraph and leidenalg for community detection, numpy and scipy for numerical computation, pandas for data handling, and matplotlib for visualizing network results.

Yes, graph construction supports building networks directly from pandas edge tables, adjacency matrices, or edge lists. This allows you to seamlessly transition from tabular data to networkx graph structures for analysis.

The best way to detect influential nodes in a social network is computing centrality measures like PageRank and betweenness centrality. These network analysis metrics highlight nodes that control information flow or hold structural importance.