Working with InfomapΒΆ

These chapters cover the practical workflow: build a network from your data, run Infomap, then read, visualise, and export the result. They are task-oriented; for the ideas behind them see Concepts, and for the smallest possible example see Quick start.

  • Building a network: every way to get your data in with infomap.run() and Network: NetworkX, igraph, SciPy sparse matrices, edge indices, edge lists, and incremental construction.

  • Running Infomap: the few options that matter, with rules of thumb for trials, seeds, directedness, and resolution.

  • Reading the Result: the immutable Result surface: scalar metrics, modules, per-node flow, the hierarchical tree, and a pandas DataFrame of assignments.

  • Visualising and exporting: draw the partition and write .tree, .clu, GraphML, or GEXF for Gephi and other tools.