3 Md @s0ddlmZddlZdgZdddZddZdS)) defaultdictNk_clique_communitiesc #sdkrtjdd|dkr,tj|}fdd|D}tt}x(|D] }x|D]}||j|qVWqLWtj}|j|xB|D]:}x4t||D]&}t |j |dkr|j ||qWqWxtj |D]}t j|VqWdS)ueFind k-clique communities in graph using the percolation method. A k-clique community is the union of all cliques of size k that can be reached through adjacent (sharing k-1 nodes) k-cliques. Parameters ---------- G : NetworkX graph k : int Size of smallest clique cliques: list or generator Precomputed cliques (use networkx.find_cliques(G)) Returns ------- Yields sets of nodes, one for each k-clique community. Examples -------- >>> from networkx.algorithms.community import k_clique_communities >>> G = nx.complete_graph(5) >>> K5 = nx.convert_node_labels_to_integers(G, first_label=2) >>> G.add_edges_from(K5.edges()) >>> c = list(k_clique_communities(G, 4)) >>> sorted(list(c[0])) [0, 1, 2, 3, 4, 5, 6] >>> list(k_clique_communities(G, 6)) [] References ---------- .. [1] Gergely Palla, Imre Derényi, Illés Farkas1, and Tamás Vicsek, Uncovering the overlapping community structure of complex networks in nature and society Nature 435, 814-818, 2005, doi:10.1038/nature03607 zk=z, k must be greater than 1.Ncs g|]}t|krt|qS)len frozenset).0c)krW/var/www/html/virt/lib/python3.6/site-packages/networkx/algorithms/community/kclique.py 2sz(k_clique_communities..)nxZ NetworkXErrorZ find_cliquesrlistappendZGraphZadd_nodes_from_get_adjacent_cliquesr intersectionZadd_edgeZconnected_componentsrunion) Gr Zcliquesmembership_dictcliquenodeZ perc_graph adj_clique componentr)r r rs"'     cCs<t}x0|D](}x"||D]}||kr|j|qWq W|S)N)setadd)rrZadjacent_cliquesnrrrr rHs  r)N) collectionsrZnetworkxr__all__rrrrrr s  A