hWjSrSSKrSSKJr S/r\"S5\R "SSS9S Sj55rg) z8Generate graphs with given degree and triangle sequence.N)py_random_staterandom_clustered_graphT)graphs returns_graphcj[U5n[U5n[R"X1[RS9nUR 5(a[R "S5e/n/nUHSnXn[US5Hn URU5 M [US5Hn URU5 M MU [U5S-S:wd[U5S-S:wa[R "S5eURU5 URU5 U(a7URUR5UR55 U(aM7U(aPUR5n UR5n UR5n URX4X4X4/5 U(aMPU$)a Generate a random graph with the given joint independent edge degree and triangle degree sequence. This uses a configuration model-like approach to generate a random graph (with parallel edges and self-loops) by randomly assigning edges to match the given joint degree sequence. The joint degree sequence is a list of pairs of integers of the form $[(d_{1,i}, d_{1,t}), \dotsc, (d_{n,i}, d_{n,t})]$. According to this list, vertex $u$ is a member of $d_{u,t}$ triangles and has $d_{u, i}$ other edges. The number $d_{u,t}$ is the *triangle degree* of $u$ and the number $d_{u,i}$ is the *independent edge degree*. Parameters ---------- joint_degree_sequence : list of integer pairs Each list entry corresponds to the independent edge degree and triangle degree of a node. create_using : NetworkX graph constructor, optional (default MultiGraph) Graph type to create. If graph instance, then cleared before populated. seed : integer, random_state, or None (default) Indicator of random number generation state. See :ref:`Randomness`. Returns ------- G : MultiGraph A graph with the specified degree sequence. Nodes are labeled starting at 0 with an index corresponding to the position in deg_sequence. Raises ------ NetworkXError If the independent edge degree sequence sum is not even or the triangle degree sequence sum is not divisible by 3. Notes ----- As described by Miller [1]_ (see also Newman [2]_ for an equivalent description). A non-graphical degree sequence (not realizable by some simple graph) is allowed since this function returns graphs with self loops and parallel edges. An exception is raised if the independent degree sequence does not have an even sum or the triangle degree sequence sum is not divisible by 3. This configuration model-like construction process can lead to duplicate edges and loops. You can remove the self-loops and parallel edges (see below) which will likely result in a graph that doesn't have the exact degree sequence specified. This "finite-size effect" decreases as the size of the graph increases. References ---------- .. [1] Joel C. Miller. "Percolation and epidemics in random clustered networks". In: Physical review. E, Statistical, nonlinear, and soft matter physics 80 (2 Part 1 August 2009). .. [2] M. E. J. Newman. "Random Graphs with Clustering". In: Physical Review Letters 103 (5 July 2009) Examples -------- >>> deg = [(1, 0), (1, 0), (1, 0), (2, 0), (1, 0), (2, 1), (0, 1), (0, 1)] >>> G = nx.random_clustered_graph(deg) To remove parallel edges: >>> G = nx.Graph(G) To remove self loops: >>> G.remove_edges_from(nx.selfloop_edges(G)) )defaultzDirected Graph not supportedrrzInvalid degree sequence) listlennx empty_graph MultiGraph is_directed NetworkXErrorrangeappendshuffleadd_edgepopadd_edges_from)joint_degree_sequence create_usingseedNGilisttlistndegreesicounttcountn1n2n3s V/var/www/html/env/lib/python3.13/site-packages/networkx/generators/random_clustered.pyrr sa`!!67 !"A q >A}}=>> E E '*GAJ'F LLO(GAJ'F LLO(  5zA~c%j1n1899LLLL  599; , %  YY[ YY[ YY[ 2(RHrh78 % H)NN)__doc__networkxrnetworkx.utilsr__all__ _dispatchablerr(r'r/sF>* # $T2j 3j r(