U 9%e @s0ddlmZddlZdgZdddZddZdS)) defaultdictNk_clique_communitiesc #sdkrtdd|dkr,t|}fdd|D}tt}|D]}|D]}|||qRqJt}|||D]6}t||D]&}t | |dkr| ||qq~t |D]}t j|VqdS)u8Find 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 -------- >>> G = nx.complete_graph(5) >>> K5 = nx.convert_node_labels_to_integers(G, first_label=2) >>> G.add_edges_from(K5.edges()) >>> c = list(nx.community.k_clique_communities(G, 4)) >>> sorted(list(c[0])) [0, 1, 2, 3, 4, 5, 6] >>> list(nx.community.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).0ckrd/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/networkx/algorithms/community/kclique.py 2s z(k_clique_communities..)nxZ NetworkXErrorZ find_cliquesrlistappendZGraphZadd_nodes_from_get_adjacent_cliquesr intersectionZadd_edgeZconnected_componentsrunion) Gr Zcliquesmembership_dictcliquenodeZ perc_graph adj_clique componentrr r rs"&  cCs4t}|D]$}||D]}||kr||qq |S)N)setadd)rrZadjacent_cliquesnrrrr rHs  r)N) collectionsrZnetworkxr__all__rrrrrr s  @