def add_list_to_cluster_dict(cl_id, cllist, cluster_dict): neighbor_list = [] if cl_id not in cluster_dict: for node in cllist: neighbor_list = union(neighbor_list, neighbor_dict[node]) cluster_dict[cl_id] = cl(cl_id, cllist, neighbor_list) return
def add_list_to_cluster_dict(cllist, graph, cluster_dict): cl_id = get_id(cllist) if cl_id not in cluster_dict: sub_graph = graph.subgraph(cllist) clustering_coeff = nx.average_clustering(sub_graph) cluster_dict[cl_id] = cl(cllist, clustering_coeff) return
def generate_cluster_dict(clusters, cluster_dict, graph): for i in clusters: i_int = [] neighbor_list = [] for string in i: i_int.append(int(string)) neighbor_list = union(neighbor_list, neighbor_dict[string]) id = get_id(i_int) if id not in cluster_dict: cluster_dict[id] = cl(id, i, neighbor_list)
def generate_cluster_dict(clusters, cluster_dict, graph): for i in clusters: i_int = [] for string in i: i_int.append(int(string)) id = get_id(i_int) if id not in cluster_dict: sub_graph = graph.subgraph(i_int) clustering_coeff = nx.average_clustering(sub_graph) cluster_dict[id] = cl(i_int, clustering_coeff)