fi :dZddlZdgZejddZdS)z@Algorithm to select influential nodes in a graph using VoteRank.Nvoterankcg}it|dkr|S||t|krt|}|r>> G = nx.Graph([(0, 1), (0, 2), (0, 3), (1, 4)]) >>> nx.voterank(G) [0, 1] The algorithm can be used both for undirected and directed graphs. However, the directed version is different in two ways: (i) nodes only vote for their in-neighbors and (ii) only the voting ability of elected node and its out-neighbors are updated: >>> G = nx.DiGraph([(0, 1), (2, 1), (2, 3), (3, 4)]) >>> nx.voterank(G) [2, 3] Notes ----- Each edge is treated independently in case of multigraphs. References ---------- .. [1] Zhang, J.-X. et al. (2016). Identifying a set of influential spreaders in complex networks. Sci. Rep. 6, 27823; doi: 10.1038/srep27823. rNc3 K|] \}}|V dSN.0_degs /var/www/development/aibuddy-work/election-extract/venv/lib/python3.11/site-packages/networkx/algorithms/centrality/voterank_alg.py zvoterank..@s&993999999c3 K|] \}}|V dSrrrs r r zvoterank..Cs&553555555rc |dS)Nrr)x vote_ranks r zvoterank..Usy|Ar)key) len is_directedsum out_degreedegreenodesrangeedgesmaxappend)Gnumber_of_nodesinfluential_nodes avgDegreenr nbrrs @r rrs`I 1vv{{  /CFF":":a&&}}?99!,,..99999CFFB 55!((**55555A> WWYY1v ! ? # #::  AIaLOOggii 5 5FAs aLOOOy~a0 0OOO==?? 5#q!!!Yq\!_4!!!"  AIaLOO 6666 7 7 7 Q<?a  $ $ $ $  ###1v ! ggajj : :FAs cN1   Y .    #IcN1$5q 9 9IcN1   : rr)__doc__networkxnx__all__ _dispatchablerrrr r+sRFF ,VVVVVVr