| import networkx as nx |
| from nose.tools import * |
| from networkx.algorithms.bipartite.cluster import cc_dot,cc_min,cc_max |
| import networkx.algorithms.bipartite as bipartite |
| |
| def test_pairwise_bipartite_cc_functions(): |
| # Test functions for different kinds of bipartite clustering coefficients |
| # between pairs of nodes using 3 example graphs from figure 5 p. 40 |
| # Latapy et al (2008) |
| G1 = nx.Graph([(0,2),(0,3),(0,4),(0,5),(0,6),(1,5),(1,6),(1,7)]) |
| G2 = nx.Graph([(0,2),(0,3),(0,4),(1,3),(1,4),(1,5)]) |
| G3 = nx.Graph([(0,2),(0,3),(0,4),(0,5),(0,6),(1,5),(1,6),(1,7),(1,8),(1,9)]) |
| result = {0:[1/3.0, 2/3.0, 2/5.0], |
| 1:[1/2.0, 2/3.0, 2/3.0], |
| 2:[2/8.0, 2/5.0, 2/5.0]} |
| for i, G in enumerate([G1, G2, G3]): |
| assert(bipartite.is_bipartite(G)) |
| assert(cc_dot(set(G[0]), set(G[1])) == result[i][0]) |
| assert(cc_min(set(G[0]), set(G[1])) == result[i][1]) |
| assert(cc_max(set(G[0]), set(G[1])) == result[i][2]) |
| |
| def test_star_graph(): |
| G=nx.star_graph(3) |
| # all modes are the same |
| answer={0:0,1:1,2:1,3:1} |
| assert_equal(bipartite.clustering(G,mode='dot'),answer) |
| assert_equal(bipartite.clustering(G,mode='min'),answer) |
| assert_equal(bipartite.clustering(G,mode='max'),answer) |
| |
| @raises(nx.NetworkXError) |
| def test_not_bipartite(): |
| bipartite.clustering(nx.complete_graph(4)) |
| |
| @raises(nx.NetworkXError) |
| def test_bad_mode(): |
| bipartite.clustering(nx.path_graph(4),mode='foo') |
| |
| def test_path_graph(): |
| G=nx.path_graph(4) |
| answer={0:0.5,1:0.5,2:0.5,3:0.5} |
| assert_equal(bipartite.clustering(G,mode='dot'),answer) |
| assert_equal(bipartite.clustering(G,mode='max'),answer) |
| answer={0:1,1:1,2:1,3:1} |
| assert_equal(bipartite.clustering(G,mode='min'),answer) |
| |
| def test_average_path_graph(): |
| G=nx.path_graph(4) |
| assert_equal(bipartite.average_clustering(G,mode='dot'),0.5) |
| assert_equal(bipartite.average_clustering(G,mode='max'),0.5) |
| assert_equal(bipartite.average_clustering(G,mode='min'),1) |
| |
| def test_ra_clustering_davis(): |
| G = nx.davis_southern_women_graph() |
| cc4 = round(bipartite.robins_alexander_clustering(G), 3) |
| assert_equal(cc4, 0.468) |
| |
| def test_ra_clustering_square(): |
| G = nx.path_graph(4) |
| G.add_edge(0, 3) |
| assert_equal(bipartite.robins_alexander_clustering(G), 1.0) |
| |
| def test_ra_clustering_zero(): |
| G = nx.Graph() |
| assert_equal(bipartite.robins_alexander_clustering(G), 0) |
| G.add_nodes_from(range(4)) |
| assert_equal(bipartite.robins_alexander_clustering(G), 0) |
| G.add_edges_from([(0,1),(2,3),(3,4)]) |
| assert_equal(bipartite.robins_alexander_clustering(G), 0) |
| G.add_edge(1,2) |
| assert_equal(bipartite.robins_alexander_clustering(G), 0) |