wgf.4dZddlmZmZmZddlZddlmZm Z m Z gdZ e dejdddd d Z dd Ze d ejddd Ze dejdZdZdZdZdZy)z3 Label propagation community detection algorithms. )Counter defaultdictdequeN)groupsnot_implemented_forpy_random_state)label_propagation_communitiesasyn_lpa_communities"fast_label_propagation_communitiesseedweight) edge_attrs)r r c#Kt|}|j|t|}t|Dcic]\}}|| }}}|r|j }|j ||j |dkDrt||||}t|j} |j|D cgc] } || | k(s | c} } ||| k7rP| ||<tj||D]2} || | k7s | |vs|j| |j| 4|rt|jEd{ycc}}wcc} w7w)uReturns communities in `G` as detected by fast label propagation. The fast label propagation algorithm is described in [1]_. The algorithm is probabilistic and the found communities may vary in different executions. The algorithm operates as follows. First, the community label of each node is set to a unique label. The algorithm then repeatedly updates the labels of the nodes to the most frequent label in their neighborhood. In case of ties, a random label is chosen from the most frequent labels. The algorithm maintains a queue of nodes that still need to be processed. Initially, all nodes are added to the queue in a random order. Then the nodes are removed from the queue one by one and processed. If a node updates its label, all its neighbors that have a different label are added to the queue (if not already in the queue). The algorithm stops when the queue is empty. Parameters ---------- G : Graph, DiGraph, MultiGraph, or MultiDiGraph Any NetworkX graph. weight : string, or None (default) The edge attribute representing a non-negative weight of an edge. If None, each edge is assumed to have weight one. The weight of an edge is used in determining the frequency with which a label appears among the neighbors of a node (edge with weight `w` is equivalent to `w` unweighted edges). seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness`. Returns ------- communities : iterable Iterable of communities given as sets of nodes. Notes ----- Edge directions are ignored for directed graphs. Edge weights must be non-negative numbers. References ---------- .. [1] Vincent A. Traag & Lovro Šubelj. "Large network community detection by fast label propagation." Scientific Reports 13 (2023): 2701. https://doi.org/10.1038/s41598-023-29610-z rN)rshuffleset enumeratepopleftremovedegree_fast_label_countmaxvalueschoicenx all_neighborsappendaddr) Gr r nodes_queue nodes_setinodecomms label_freqsmax_freqcommnbrs t/home/mcse/projects/flask/flask-venv/lib/python3.12/site-packages/networkx/algorithms/community/label_propagation.pyr r sWf(KLLAI%.aL 1DT1W 1E 1 ""$ 88D>A +AudFCK;--/0H;;"-O$T1Bh1NODT{d""d ++At4+CSzT)c.B#**3/! c*+) 2e}##%%%7 2P&sA6E E A0E5 EE5E=E&E)EEEc ||js5tt|jt j ||}|St t}||D]!}|||xxt|||z cc<#|jr=|j|D]+}|||xxt|j||z cc<-|St t}|j||dD]\}}}|||xx|z cc<|jr-|j||dD]\}}}|||xx|z cc<|S)zComputes the frequency of labels in the neighborhood of a node. Returns a dictionary keyed by label to the frequency of that label. datadefault) is_multigraphrmapgetrrrintlen is_directedpredfloatedgesin_edges)rr#r"r r$r'_ws r(rris^ ~ !#eii1A1A!T1J"KLK. '&c*Kw =E#J'3qws|+<<' =}}66$<FCc +s166$<3D/EE+F "%( FA> )IAsA c #q ( # ) ==?ZZ61ZE - QE#J'1,' - c#Kt|Dcic]\}}|| }}}d}|rd}t|}|j||D]}||s |#tt |j ||} n`. Returns ------- communities : iterable Iterable of communities given as sets of nodes. Notes ----- Edge weight attributes must be numerical. References ---------- .. [1] Raghavan, Usha Nandini, Réka Albert, and Soundar Kumara. "Near linear time algorithm to detect community structures in large-scale networks." Physical Review E 76.3 (2007): 036106. TFNr*r+)rlistrrr/r0rr5r6rritemsrr)rr r r!nlabelscontnodesr" label_freqr8vwtr%labelfreq best_labelss r(r r s_h )| ,tq!ad ,F , D Q U DT7 ~%SQtW%=> )/ !61 E0HAq"vay)R/)0:,,./H)3)9)9);%%tx?OKd|;.#{{;7t ?  Lf~$$&&&S-:'s:E D5B=E D;)D;- E8EE/E0Edirectedct|}t|Dcic]\}}|| }}}t||s9|jD]\}}|D]}t |||t||s9t t }|jD]\} } || j| |jScc}}w)abGenerates community sets determined by label propagation Finds communities in `G` using a semi-synchronous label propagation method [1]_. This method combines the advantages of both the synchronous and asynchronous models. Not implemented for directed graphs. Parameters ---------- G : graph An undirected NetworkX graph. Returns ------- communities : iterable A dict_values object that contains a set of nodes for each community. Raises ------ NetworkXNotImplemented If the graph is directed References ---------- .. [1] Cordasco, G., & Gargano, L. (2010, December). Community detection via semi-synchronous label propagation algorithms. In Business Applications of Social Network Analysis (BASNA), 2010 IEEE International Workshop on (pp. 1-8). IEEE. ) _color_networkr_labeling_completer> _update_labelrrrr) rcoloringkrDlabelingcolorrBr?clustersr"rFs r(r r s>a H!*1.A1.H. 1-$NN, .LE5 .a1- . .!1- 3H~~'" eD!" ?? /s B:ci}tjj|}|jD]$\}}||vr||j ||h||<&|S)zColors the network so that neighboring nodes all have distinct colors. Returns a dict keyed by color to a set of nodes with that color. )rrN greedy_colorr>r)rrNcolorsr"rQs r(rKrKsb H [[ % %a (F||~% e H  UO   %#fHUO % Or:c0tfdDS)zDetermines whether or not LPA is done. Label propagation is complete when all nodes have a label that is in the set of highest frequency labels amongst its neighbors. Nodes with no neighbors are considered complete. c3fK|](}t|dkDs|t|v*yw)rN)r2_most_frequent_labels).0rDrrPs r( z%_labeling_complete..1s9AB3qQRt9WX= ,Q!<<s11)all)rPrs``r(rLrL)s FG r:c||s|hStfd||D}t|j}|jDchc] \}}||k(s |c}}Scc}}w)zReturns a set of all labels with maximum frequency in `labeling`. Input `labeling` should be a dict keyed by node to labels. c3(K|] }| ywN)rYqrPs r(rZz(_most_frequent_labels..As1AHQK1s)rrrr>)r"rPrfreqsr%rFrGs ` r(rXrX6sf T7 141 1E5<<>"H%*[[] GkeTdh6FE GG Gs  A&A&ct|||}t|dk(r|j||<yt|dkDr|||vrt|||<yyy)zUpdates the label of a node using the Prec-Max tie breaking algorithm The algorithm is explained in: 'Community Detection via Semi-Synchronous Label Propagation Algorithms' Cordasco and Gargano, 2011 r*N)rXr2popr)r"rPr high_labelss r(rMrMFs` (h:K ;1$* [ A  D> , -HTN - r:r^)NN)__doc__ collectionsrrrnetworkxrnetworkx.utilsrrr__all__ _dispatchabler rr r rKrLrXrMr_r:r(rks43GG X&48tS&'S&l FX&[''['|Z )!)X   H .r: