Mercurial > repos > shellac > sam_consensus_v3
diff env/lib/python3.9/site-packages/networkx/algorithms/hierarchy.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/env/lib/python3.9/site-packages/networkx/algorithms/hierarchy.py Mon Mar 22 18:12:50 2021 +0000 @@ -0,0 +1,47 @@ +""" +Flow Hierarchy. +""" +import networkx as nx + +__all__ = ["flow_hierarchy"] + + +def flow_hierarchy(G, weight=None): + """Returns 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 : key,optional (default=None) + Attribute to use for node 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 + """ + if not G.is_directed(): + raise nx.NetworkXError("G must be a digraph in flow_hierarchy") + scc = nx.strongly_connected_components(G) + return 1.0 - sum(G.subgraph(c).size(weight) for c in scc) / float(G.size(weight))