a h@s,dZddlZdgZejdddddZdS)z Flow Hierarchy. Nflow_hierarchyweight)Z edge_attrscsBstdt}dtfdd|DS)aReturns 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 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%G must be a digraph in flow_hierarchyc3s|]}|VqdS)N)Zsubgraphsize).0cGrK/var/www/auris/lib/python3.9/site-packages/networkx/algorithms/hierarchy.py 0z!flow_hierarchy..)Z is_directednxZ NetworkXErrorZstrongly_connected_componentssumr)r rZsccr rr r s$  )N)__doc__Znetworkxr__all__ _dispatchrr r r r s