o ]ZŽhúã@s,dZddlZdgZejddddd„ƒZdS)z Flow Hierarchy. éNÚflow_hierarchyÚweight)Z edge_attrscsVt ˆ¡r t d¡‚ˆ ¡st d¡‚t ˆ¡}dt‡‡fdd„|Dƒƒˆ ˆ¡S)aíReturns 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_hierarchyéc3s |] }ˆ |¡ ˆ¡VqdS©N)ZsubgraphÚsize)Ú.0Úc©ÚGr©úL/var/www/auris/lib/python3.10/site-packages/networkx/algorithms/hierarchy.pyÚ 9s€z!flow_hierarchy..)ÚnxZis_emptyZ NetworkXErrorZ is_directedZstrongly_connected_componentsÚsumr)r rZsccr r r r s *   &r)Ú__doc__ZnetworkxrÚ__all__Z _dispatchablerr r r r Ús