Notebook examples¶
These examples are rendered from the Jupyter notebooks in
examples/notebooks. They are published as regular documentation pages so
they can be searched, linked, and read without opening Jupyter.
Choose a notebook¶
Infomap quickstart — first end-to-end Infomap workflow in Python.
Compare Infomap and Louvain with NetworkX — compare Infomap and Louvain with NetworkX when your graph already lives in NetworkX.
Compare Infomap, Louvain, and Leiden with igraph — compare Infomap, Louvain, and Leiden when you want igraph-native clustering objects.
Compare Infomap and Leiden in a Scanpy workflow — compare Infomap and Leiden in an AnnData and Scanpy-style workflow.
Run Infomap on GraphRAG-style tables — run Infomap on GraphRAG-style entity and relationship tables and compare with Leiden.
Run Infomap on HPC — native CLI recipes for scheduler-aware HPC runs and Python shard merging.
Additional tutorial notebooks¶
The notebook source tree also includes companion material for Community Detection with the Map Equation and Infomap: Theory and Applications:
the two-level map equation;
the two-level search phase and solution landscapes;
memory, multilayer, temporal, and multi-body network models;
networks with metadata, bipartite structure, and incomplete data;
map equation centrality, similarity, bioregions, and model selection with correlational data.
Those source notebooks are available in examples/notebooks. Some require external research code or additional data-processing packages and are not rendered in this first docs set.
Run locally¶
From an Infomap source checkout:
python -m pip install -e '.[notebooks]'
cd examples/notebooks
jupyter lab