def test_plot_com_properties_relation(self):
g = nx.karate_club_graph()
coms = algorithms.louvain(g)
coms2 = algorithms.label_propagation(g)
viz.plot_com_properties_relation([coms, coms2], evaluation.size,
evaluation.internal_edge_density)
plt.savefig("cluster.pdf")
os.remove("cluster.pdf")
viz.plot_com_properties_relation(coms, evaluation.size,
evaluation.internal_edge_density)
plt.savefig("cluster.pdf")
os.remove("cluster.pdf")
def draw_plot_map(list_of_communities, number):
lmplot = viz.plot_com_properties_relation(list_of_communities,
evaluation.size,
evaluation.internal_edge_density)
filename = "communities/size_vs_internal_density" + str(number) + ".png"
lmplot = viz.plot_com_properties_relation(
list_of_communities, evaluation.size,
evaluation.triangle_participation_ratio)
filename = "communities/size_vs_triangle_participation_ratio" + str(
number) + ".png"
plt.savefig(filename)
lmplot = viz.plot_com_properties_relation(
list_of_communities, evaluation.average_internal_degree,
evaluation.fraction_over_median_degree)
filename = "communities/internal_degree_vs_fraction_over_median_degree" + str(
number) + ".png"
plt.savefig(filename)
lmplot = viz.plot_com_properties_relation(list_of_communities,
evaluation.conductance,
evaluation.expansion)
filename = "communities/outside_vs_inside_edges" + str(number) + ".png"
plt.savefig(filename)
lmplot = viz.plot_com_properties_relation(list_of_communities,
evaluation.edges_inside,
evaluation.cut_ratio)
filename = "communities/edges_inside_vs_cut_ratio" + str(number) + ".png"
plt.savefig(filename)
lmplot = viz.plot_com_properties_relation(list_of_communities,
evaluation.internal_edge_density,
evaluation.modularity_density)
filename = "communities/internal_edge_density_vs_modularity_density" + str(
number) + ".png"
plt.savefig(filename)
communities_set.add(frozenset(com_nodes))
# build the nodeclustering object
ground_truth_com = NodeClustering(communities=communities_set, graph=nx_g, method_name="ground_truth")
#%% define and plot ground truth
# define the positions here so all the cluster plots in the loop are the same structure
pos = nx.fruchterman_reingold_layout(nx_g)
#%%
# plot the original network with the ground truth communities
viz.plot_network_clusters(nx_g, ground_truth_com, pos, figsize=(5, 5))
#nx.draw_networkx_labels(nx_g, pos=pos)
plt.title(f'Ground Truth of {graph_name}')
plt.show()
#%% evaluate ground communitiy metrics
viz.plot_com_properties_relation(ground_truth_com, evaluation.size,
evaluation.internal_edge_density)
plt.show()
#%%
viz.plot_com_stat(ground_truth_com, evaluation.conductance)
plt.show()
viz.plot_com_stat(ground_truth_com, evaluation.average_internal_degree)
plt.show()
algo_dict = {'louvain': algorithms.louvain,
'leidan': algorithms.leiden,
'greed_modularity': algorithms.greedy_modularity,
'label_prop': algorithms.label_propagation,
'walktrap': algorithms.walktrap,
'infomap': algorithms.infomap,