+ jORt^RIt^RIt^RIHt^RIt^RIHtH t .ROt ]P!RRR7]!^^.4RRl44t ] !^4]P!RRRR7RR l44t ]P!R RRR7RR l4t]P!RRR7RR l4t]P!RRR7RR l4t] !^4]P!RRR7RRl44t] !^4]P!RRR7RRl44t] !^4]P!RRR7RRl44tRtR#)z0 Generators and functions for bipartite graphs. N)reduce)nodes_or_numberpy_random_stateT)graphs returns_graphca\P!^V4pVP4'd\P!R4hVwrVwpo\ V\ P 4'd9\ V\ P 4'dSUu.uF qPV,NK upoVPV^R7VPS^R7\V4\V4\S4,8wd\P!R4hVPV3RlV44R\V4 R\S4 R2VPR&V#uupi) aReturns the complete bipartite graph `K_{n_1,n_2}`. The graph is composed of two partitions with nodes 0 to (n1 - 1) in the first and nodes n1 to (n1 + n2 - 1) in the second. Each node in the first is connected to each node in the second. Parameters ---------- n1, n2 : integer or iterable container of nodes If integers, nodes are from `range(n1)` and `range(n1, n1 + n2)`. If a container, the elements are the nodes. create_using : NetworkX graph instance, (default: nx.Graph) Return graph of this type. Notes ----- Nodes are the integers 0 to `n1 + n2 - 1` unless either n1 or n2 are containers of nodes. If only one of n1 or n2 are integers, that integer is replaced by `range` of that integer. The nodes are assigned the attribute 'bipartite' with the value 0 or 1 to indicate which bipartite set the node belongs to. This function is not imported in the main namespace. To use it use nx.bipartite.complete_bipartite_graph Directed Graph not supported bipartitez,Inputs n1 and n2 must contain distinct nodesc3<<"TFpSFq!V3xK K R#5iN).0uvbottoms& i/home/luca/screen-checking/.venv/lib/python3.14/site-packages/networkx/algorithms/bipartite/generators.py +complete_bipartite_graph..As9S&QV&VSszcomplete_bipartite_graph(z, )name) nx empty_graph is_directed NetworkXError isinstancenumbersIntegraladd_nodes_fromlenadd_edges_fromgraph)n1n2 create_usingGtopirs&&& @rcomplete_bipartite_graphr(s: q,'A}}=>>GBJB"g&&''Jr7;K;K,L,L"()&Qq&&&)SA&Vq) 1vSCK''MNN9S991#c(2c&k]!LAGGFO H*sEbipartite_configuration_model)rrrca a\P!^V\PR7pVP4'd\P!R4h\ V4p\ V4p\ V4p\ V4pWx8Xg\P!RV RV 24h\WEV4p\ V4^8Xg\V4^8XdV#\V4U u.uFq.W ,,NK p p V U U u.uF qFqNK K up p o \WUV,4U u.uFq.WV, ,,NK p p V U U u.uF qFqNK K up p oVPS 4VPS4VPV V3Rl\V444RVn V#uup iuup p iuup iuup p i)a{Returns a random bipartite graph from two given degree sequences. Parameters ---------- aseq : list Degree sequence for node set A. bseq : list Degree sequence for node set B. create_using : NetworkX graph instance, optional Return graph of this type. seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness`. The graph is composed of two partitions. Set A has nodes 0 to (len(aseq) - 1) and set B has nodes len(aseq) to (len(bseq) - 1). Nodes from set A are connected to nodes in set B by choosing randomly from the possible free stubs, one in A and one in B. Notes ----- The sum of the two sequences must be equal: sum(aseq)=sum(bseq) If no graph type is specified use MultiGraph with parallel edges. If you want a graph with no parallel edges use create_using=Graph() but then the resulting degree sequences might not be exact. The nodes are assigned the attribute 'bipartite' with the value 0 or 1 to indicate which bipartite set the node belongs to. This function is not imported in the main namespace. To use it use nx.bipartite.configuration_model defaultr/invalid degree sequences, sum(aseq)!=sum(bseq),,c3H<"TFpSV,SV,.xK R#5ir r )rr'astubsbstubss& rr&configuration_model..sA[fQi+[s"r)) rr MultiGraphrrrsum_add_nodes_with_bipartite_labelmaxrangeshuffler r)aseqbseqr$seedr%lenalenbsumasumbrstubssubseqxr0r1s&&&& @@rconfiguration_modelrCFsF q, >A}}=>> t9D t9D t9D t9D <=dV1TF K   (6A 4yA~Ta%*$K 0KqS47]]KE 0# 4eFVaVae 4F+0d{+C D+CaS4D> ! !+CE D# 4eFVaVae 4F LLLLAU4[AA ,AF H 1 4 D 4sF4>F9(F?Gbipartite_havel_hakimi_graphc\P!^V\PR7pVP4'd\P!R4h\ V4p\ V4p\ V4p\ V4pWg8Xg\P!RV RV 24h\W4V4p\ V4^8Xg\V4^8XdV#\V4Uu.uF qV,V.NK p p\WDV,4Uu.uFqW, ,V.NK p pV P4V 'dV P4wrV ^8XdMnV P4W)RFSp V ^,pVPW4V ^;;,^,uu&V ^,^8XgKBV PV 4KU KRVnV#uupiuupi)aEReturns a bipartite graph from two given degree sequences using a Havel-Hakimi style construction. The graph is composed of two partitions. Set A has nodes 0 to (len(aseq) - 1) and set B has nodes len(aseq) to (len(bseq) - 1). Nodes from the set A are connected to nodes in the set B by connecting the highest degree nodes in set A to the highest degree nodes in set B until all stubs are connected. Parameters ---------- aseq : list Degree sequence for node set A. bseq : list Degree sequence for node set B. create_using : NetworkX graph instance, optional Return graph of this type. Notes ----- The sum of the two sequences must be equal: sum(aseq)=sum(bseq) If no graph type is specified use MultiGraph with parallel edges. If you want a graph with no parallel edges use create_using=Graph() but then the resulting degree sequences might not be exact. The nodes are assigned the attribute 'bipartite' with the value 0 or 1 to indicate which bipartite set the node belongs to. This function is not imported in the main namespace. To use it use nx.bipartite.havel_hakimi_graph r+rr-r.NrDrrr3rrrr4r5r6r7sortpopadd_edgeremover)r9r:r$r%naseqnbseqr>r?rr0r1degreertargets&&& rhavel_hakimi_graphrOsB q, >A}}=>> IE IE t9D t9D <=dV1TF K   (%8A 4yA~Ta%*%L 1LqAwlLF 1,1%,G H,GqAI",GF H KKM jjl  Q;  WX&Fq A JJq  1INIayA~ f% ',AF H#2 H G Gc\P!^V\PR7pVP4'd\P!R4h\ V4p\ V4p\ V4p\ V4pWg8Xg\P!RV RV 24h\W4V4p\ V4^8Xg\V4^8XdV#\V4Uu.uF qV,V.NK p p\WDV,4Uu.uFqW, ,V.NK p pV P4V P4V 'dyV P4wrV ^8XdM^V ^V FSp V ^,pVPW4V ^;;,^,uu&V ^,^8XgKBV PV 4KU KRVnV#uupiuupi)aHReturns a bipartite graph from two given degree sequences using a Havel-Hakimi style construction. The graph is composed of two partitions. Set A has nodes 0 to (len(aseq) - 1) and set B has nodes len(aseq) to (len(bseq) - 1). Nodes from set A are connected to nodes in the set B by connecting the highest degree nodes in set A to the lowest degree nodes in set B until all stubs are connected. Parameters ---------- aseq : list Degree sequence for node set A. bseq : list Degree sequence for node set B. create_using : NetworkX graph instance, optional Return graph of this type. Notes ----- The sum of the two sequences must be equal: sum(aseq)=sum(bseq) If no graph type is specified use MultiGraph with parallel edges. If you want a graph with no parallel edges use create_using=Graph() but then the resulting degree sequences might not be exact. The nodes are assigned the attribute 'bipartite' with the value 0 or 1 to indicate which bipartite set the node belongs to. This function is not imported in the main namespace. To use it use nx.bipartite.reverse_havel_hakimi_graph r+rr-r.$bipartite_reverse_havel_hakimi_graphrF)r9r:r$r%r<r=r>r?rr0r1rMrrNs&&& rreverse_havel_hakimi_graphrSsB q, >A}}=>> t9D t9D t9D t9D <=dV1TF K   (6A 4yA~Ta%*$K 0KqAwlKF 0+0d{+C D+CaAH~q!+CF D KKM KKM jjl  Q; Qv&Fq A JJq  1INIayA~ f% '4AF H#1 DrPc\P!^V\PR7pVP4'd\P!R4h\ V4p\ V4p\ V4p\ V4pWg8Xg\P!RV RV 24h\W4V4p\ V4^8Xg\V4^8XdV#\V4Uu.uF qV,V.NK p p\WDV,4Uu.uFqW, ,V.NK p pV 'Ed"V P4V P4wrV ^8XdMV P4V ^V ^,p W)V ^,,Rp\W4UUu.uF qFpVNK K ppp\ V4\ V 4\ V4,8d VPVP44VFSpV^,pVPW4V^;;,^,uu&V^,^8XgKBV PV4KU EK*RVnV#uupiuupiuuppi)a{Returns a bipartite graph from two given degree sequences using an alternating Havel-Hakimi style construction. The graph is composed of two partitions. Set A has nodes 0 to (len(aseq) - 1) and set B has nodes len(aseq) to (len(bseq) - 1). Nodes from the set A are connected to nodes in the set B by connecting the highest degree nodes in set A to alternatively the highest and the lowest degree nodes in set B until all stubs are connected. Parameters ---------- aseq : list Degree sequence for node set A. bseq : list Degree sequence for node set B. create_using : NetworkX graph instance, optional Return graph of this type. Notes ----- The sum of the two sequences must be equal: sum(aseq)=sum(bseq) If no graph type is specified use MultiGraph with parallel edges. If you want a graph with no parallel edges use create_using=Graph() but then the resulting degree sequences might not be exact. The nodes are assigned the attribute 'bipartite' with the value 0 or 1 to indicate which bipartite set the node belongs to. This function is not imported in the main namespace. To use it use nx.bipartite.alternating_havel_hakimi_graph r+rr-r.N(bipartite_alternating_havel_hakimi_graph)rrr3rrrr4r5r6r7rGrHzipappendrIrJr)r9r:r$r%rKrLr>r?rr0r1rMrsmalllargezrBr@rNs&&& ralternating_havel_hakimi_graphr[#sD q, >A}}=>> IE IE t9D t9D <=dV1TF K   (%8A 4yA~Ta$)%L 1LqAwlLF 1,1%,G H,GqAI",GF H & jjl  Q;  q6Q;'&A+-01-9-qq!q-9 u:E SZ/ / LL %Fq A JJq  1INIayA~ f% 8AF H+2 H:sIIIc\P!^V\PR7pVP4'd\P!R4hV^8d\P!RV R24h\ V4p\ WE^4p\V4Uu.uFqf.W,,NK ppV'Ed(V^,'dV^,^,pV^,PV4VP4V8g\ V4V8Xd2\ V4p VPV ^R7VPW4K\V\ V44U u.uFq.VPV 4,NK p p \RV 4p VPV 4p VPV ^R7VPW4EK VPV^,4EK0RVnV#uupiuup i)aCreate a bipartite graph with a preferential attachment model from a given single degree sequence. The graph is composed of two partitions. Set A has nodes 0 to (len(aseq) - 1) and set B has nodes starting with node len(aseq). The number of nodes in set B is random. Parameters ---------- aseq : list Degree sequence for node set A. p : float Probability that a new bottom node is added. create_using : NetworkX graph instance, optional Return graph of this type. seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness`. References ---------- .. [1] Guillaume, J.L. and Latapy, M., Bipartite graphs as models of complex networks. Physica A: Statistical Mechanics and its Applications, 2006, 371(2), pp.795-813. .. [2] Jean-Loup Guillaume and Matthieu Latapy, Bipartite structure of all complex networks, Inf. Process. Lett. 90, 2004, pg. 215-221 https://doi.org/10.1016/j.ipl.2004.03.007 Notes ----- The nodes are assigned the attribute 'bipartite' with the value 0 or 1 to indicate which bipartite set the node belongs to. This function is not imported in the main namespace. To use it use nx.bipartite.preferential_attachment_graph r+rz probability z > 1r cW,#r r )rBys&&r/preferential_attachment_graph..sae'bipartite_preferential_attachment_model)rrr3rrrr5r7rJrandomadd_noderIrMrchoicer) r9pr$r;r%rKrvvsourcerNbbbbbstubss &&&& rpreferential_attachment_graphrlqsR q, >A}}=>>1uaS566 IE'!4A!&u .A#--B . "eeU1XF qELL {{}q CFeOQ 6Q / 6*16uc!f1EF1EAcAHHQK''1EF !3R8W- 6Q / 6* "Q% 6AF H' /Gs G, !G1c\P!4p\WPV4pV'd\P!V4pRV RV RV R2VnV^8:dV#V^8d\P !W4#\ P!RV, 4p^pRpWp8d\ P!RVP4, 4p V^,\W, 4,pW8dWp8dW, pV^,pKWp8gKwVPWpV,4KV'd^pRpWp8d\ P!RVP4, 4p V^,\W, 4,pW8dWp8dW, pV^,pKWp8gKwVPW,V4KV#)uReturns a bipartite random graph. This is a bipartite version of the binomial (Erdős-Rényi) graph. The graph is composed of two partitions. Set A has nodes 0 to (n - 1) and set B has nodes n to (n + m - 1). Parameters ---------- n : int The number of nodes in the first bipartite set. m : int The number of nodes in the second bipartite set. p : float Probability for edge creation. seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness`. directed : bool, optional (default=False) If True return a directed graph Notes ----- The bipartite random graph algorithm chooses each of the n*m (undirected) or 2*nm (directed) possible edges with probability p. This algorithm is $O(n+m)$ where $m$ is the expected number of edges. The nodes are assigned the attribute 'bipartite' with the value 0 or 1 to indicate which bipartite set the node belongs to. This function is not imported in the main namespace. To use it use nx.bipartite.random_graph See Also -------- gnp_random_graph, configuration_model References ---------- .. [1] Vladimir Batagelj and Ulrik Brandes, "Efficient generation of large random networks", Phys. Rev. E, 71, 036113, 2005. zfast_gnp_random_graph(r.rg?) rGraphr5DiGraphrr(mathlogrcintrI) nmrfr;directedr%lprwlrs &&&&& r random_graphrzs\\  A'a0A JJqM%aS!AaS 2AFAvAv**100 #' B A A % XXcDKKM) * ECL fAAA 5 JJqa%   e# -.BABG $A&QUEEu 15!$ Hracz\P!4p\WPV4pV'd\P!V4pRV RV RV R2VnV^8XgV^8XdV#W,pW&8d\P !WVR7#VP RR7UUu.uFwrVR,^8XgKVNK ppp\\V4\V4, 4p ^p W8dNVPV4p VPV 4p WV ,9dK7VPW4V ^, p KSV#uuppi)aqReturns a random bipartite graph G_{n,m,k}. Produces a bipartite graph chosen randomly out of the set of all graphs with n top nodes, m bottom nodes, and k edges. The graph is composed of two sets of nodes. Set A has nodes 0 to (n - 1) and set B has nodes n to (n + m - 1). Parameters ---------- n : int The number of nodes in the first bipartite set. m : int The number of nodes in the second bipartite set. k : int The number of edges seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness`. directed : bool, optional (default=False) If True return a directed graph Examples -------- >>> G = nx.bipartite.gnmk_random_graph(10, 20, 50) See Also -------- gnm_random_graph Notes ----- If k > m * n then a complete bipartite graph is returned. This graph is a bipartite version of the `G_{nm}` random graph model. The nodes are assigned the attribute 'bipartite' with the value 0 or 1 to indicate which bipartite set the node belongs to. This function is not imported in the main namespace. To use it use nx.bipartite.gnmk_random_graph zbipartite_gnm_random_graph(r.r)r$T)datar ) rror5rprr(nodeslistsetrerI) rtrukr;rvr% max_edgesdr&r edge_countrrs &&&&& rgnmk_random_graphrsX  A'a0A JJqM*1#Qqc1#Q 7AFAvaI~**1a@@d+ C+q~/B11+C C #a&3s8# $FJ . KK  KK  !9  JJq  !OJ H Ds D73D7c 8VP\W,44\\\V4^.V,44pVP \\\WV,4^.V,444\ P !WR4V#)r )rr7dictrVupdaterset_node_attributes)r%r<r=ris&&& rr5r5VsnU4;'( StqcDj )*AHHT#eD+.d ; <=1- Hra)rCrOrSr[rlrzrr(r )NN)NF)__doc__rqr functoolsrnetworkxrnetworkx.utilsrr__all__ _dispatchabler(rCrOrSr[rlrzrr5r rarrs ; T2!Q) 3) X6tSWXC YC L5dRVWG XG TT2F 3F RT2J 3J ZT2C 3C LT2R 3R jT2A 3A H ra