Top-level functions

Graph-package entry points

infomap.find_communities(g: Any, *, weight: str | None = 'weight', node_id: str = 'node_id', layer_id: str = 'layer_id', multilayer_inter_intra_format: bool = True, initial_partition: Any = None, module_attribute: str | None = None, flow_attribute: str | None = None, **infomap_options: Any) → list[set[Any]]

Find communities in a NetworkX-style graph.

This helper is duck-typed and does not import NetworkX. It accepts the same graph objects as Infomap.add_networkx_graph(), runs Infomap, and returns communities using the original graph node labels.

Parameters:

  • g (nx.Graph) – A NetworkX-compatible graph.

  • weight (str or None, optional) – Key to look up link weight in edge data if present. Default "weight". Use None to treat every edge as weight 1.

  • node_id (str, optional) – Node attribute for physical node ids, implying a state network.

  • layer_id (str, optional) – Node attribute for layer ids, implying a multilayer network.

  • multilayer_inter_intra_format (bool, optional) – Use intra/inter format to simulate inter-layer links. Default True.

  • module_attribute (str, optional) – If set, write each node’s module id back to this node attribute on g.

  • flow_attribute (str, optional) – If set, write each node’s flow back to this node attribute on g.

  • initial_partition (mapping, optional) – Initial module assignment passed to infomap.Infomap.run(). Keys may use the original NetworkX node labels.

  • **infomap_options – Keyword arguments passed to infomap.Infomap. By default, silent=True and no_file_output=True are used unless explicitly overridden.

Returns:

A partition of g.nodes grouped by top-level Infomap module.

Return type:

list of set

infomap.find_igraph_communities(g: Any, *, edge_weights: str | Iterable[Any] | None = None, vertex_weights: Any = None, trials: int = 10, node_id: str = 'node_id', layer_id: str = 'layer_id', multilayer_inter_intra_format: bool = True, module_attribute: str | None = None, flow_attribute: str | None = None, **infomap_options: Any) → Any

Find communities in a python-igraph graph.

infomap.tl.infomap(adata: Any, *, adjacency: Any = None, directed: bool = False, use_weights: bool = True, key_added: str = 'infomap', neighbors_key: str | None = None, obsp: str | None = None, copy: bool = False, args: str | None = None, **infomap_options: Any) → Any

Run Infomap on an AnnData observation graph.

This function follows Scanpy tl conventions: by default it reads adata.obsp["connectivities"], writes categorical module labels to adata.obs[key_added], and stores run metadata in adata.uns[key_added]. Scanpy itself is not imported.

Information-theoretic primitives

infomap.entropy(p)
infomap.perplexity(p)
infomap.plogp(p)