+ )iNRt^RItR.t]P!RR7RRl4tR#)z Flow Hierarchy. Nflow_hierarchyweight) edge_attrscTaa\P!S4'd\P!R4hSP4'g\P!R4h\P!S4p^\ VV3RlV44SP S4, , #)aqReturns the flow hierarchy of a directed network. Flow hierarchy is defined as the fraction of edges not participating in cycles in a directed graph [1]_. Parameters ---------- G : DiGraph or MultiDiGraph A directed graph weight : string, optional (default=None) Attribute to use for edge weights. If None the weight defaults to 1. Returns ------- h : float Flow hierarchy value Raises ------ NetworkXError If `G` is not a directed graph or if `G` has no edges. Notes ----- The algorithm described in [1]_ computes the flow hierarchy through exponentiation of the adjacency matrix. This function implements an alternative approach that finds strongly connected components. An edge is in a cycle if and only if it is in a strongly connected component, which can be found in $O(m)$ time using Tarjan's algorithm. References ---------- .. [1] Luo, J.; Magee, C.L. (2011), Detecting evolving patterns of self-organizing networks by flow hierarchy measurement, Complexity, Volume 16 Issue 6 53-61. DOI: 10.1002/cplx.20368 http://web.mit.edu/~cmagee/www/documents/28-DetectingEvolvingPatterns_FlowHierarchy.pdf z-flow_hierarchy not applicable to empty graphsz%G must be a digraph in flow_hierarchyc3d<"TF%pSPV4PS4xK' R#5iN)subgraphsize).0cGrs& [/var/www/html/photoedit/myenv/lib/python3.14/site-packages/networkx/algorithms/hierarchy.py !flow_hierarchy..9s';s!1::a=%%f--ss-0)nxis_empty NetworkXError is_directedstrongly_connected_componentssumr )r rsccsff r rr suT {{1~~NOO ==??FGG * *1 -C s;s;;affVnL LLr)__doc__networkxr__all__ _dispatchablerrr rs8  X&.M'.Mr