scientific-network-visualization

To detect communities in a network graph and visualize results interactively, input your edgelist or adjacency matrix into a pipeline leveraging NetworkX, Louvain or Leiden methods, and PyVis for web-friendly rendering.

Yes, you can use Louvain and Leiden algorithms for community detection on dynamic or time-evolving networks. The pipeline supports analyzing correlation-based networks and computing centrality metrics for these evolving graph structures.

The best way to analyze a protein interaction network to identify hub proteins is constructing a graph with NetworkX, computing centrality metrics to find hubs, and applying Leiden or Louvain methods to identify community modules.

Yes, this network visualization approach works with an adjacency matrix input. The workflow accepts both edgelist and adjacency matrix formats to construct graphs, compute basic statistics, and generate interactive PyVis visualizations.

To compute centrality metrics for a social graph and find influential nodes, construct your graph using NetworkX and run the integrated analysis pipeline to evaluate node importance and detect community structures.

PyVis generates web-friendly interactive visualizations for collaboration networks, but rendering performance may decrease with extremely large graphs. The pipeline maintains reproducibility and modularity by exposing a clear structure in SKILL.md for analysis.