military_alliances.py

"""
Runs the analysis of the military alliances network found in the
paper 'A Bayesian nonparametric latent space approach to modeling evolving
communities in dynamic networks' by Joshua Loyal and Yuguo Chen
"""

from dynetlsm import DynamicNetworkHDPLPCM
from dynetlsm.datasets import load_alliances
from dynetlsm.plots import (
    plot_traces,
    alluvial_plot,
    plot_latent_space
)


# Load military alliances networks
Y, names = load_alliances()

# Fit HDP-LPCM
# NOTE: This will take days to sample!
model = DynamicNetworkHDPLPCM(n_iter=400000,
                              tune=50000,
                              burn=50000,
                              tune_interval=1000,
                              random_state=42,
                              n_components=25,
                              selection_type='vi',
                              is_directed=False).fit(Y)

# Trace plots
fig, ax = plot_traces(model, figsize=(10, 12))
fig.savefig('alliances_traces.png', dpi=300)

# alluvial diagram
fig, ax = alluvial_plot(model.z_, figsize=(10, 5))
fig.savefig('alliances_alluvial.png', dpi=300)

# latent space visualizations
for t in range(Y.shape[0]):
    fig, ax = plot_latent_space(
        model, figsize=(30, 30), t=t,
        textsize=30,
        node_size=500,
        mutation_scale=20,
        linewidth=1.0,
        connectionstyle='arc3,rad=0.2',
        title_text=None,
        plot_group_sigma=True,
        node_names=names,
        node_textsize=20,
        repel_strength=0.3,
        mask_groups=[1], # NOTE: this may not be background on other settings!
        only_show_connected=True,
        number_nodes=True,
        border=1.0)
    fig.savefig('alliances_latent_space_t{}.png'.format(t), dpi=300)