def test_plot_com_stat(self):
g = nx.karate_club_graph()
coms = algorithms.louvain(g)
coms2 = algorithms.label_propagation(g)
viz.plot_com_stat([coms, coms2], evaluation.size)
plt.savefig("cluster.pdf")
os.remove("cluster.pdf")
viz.plot_com_stat(coms, evaluation.size)
plt.savefig("cluster.pdf")
os.remove("cluster.pdf")
def draw_cluster_violin_map(list_of_communities):
viz.plot_com_stat(list_of_communities, evaluation.size)
plt.savefig("communities/size.png")
viz.plot_com_stat(list_of_communities, evaluation.average_internal_degree)
plt.savefig("communities/average_internal_degree.png")
viz.plot_com_stat(list_of_communities, evaluation.edges_inside)
plt.savefig("communities/edges_inside.png")
viz.plot_com_stat(list_of_communities, evaluation.cut_ratio)
plt.savefig("communities/cut_ratio.png")
# 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,
'eigenvector': algorithms.eigenvector,
'spinglass': algorithms.spinglass}
# %% define algorithms and their names to iterate through
algo_dict = {'louvain': algorithms.louvain,