from py3plex.core import multinet
from py3plex.wrappers import train_node2vec_embedding
from py3plex.visualization.embedding_visualization import embedding_visualization
from py3plex.visualization.embedding_visualization import embedding_tools
import json
## load network in GML
multilayer_network = multinet.multi_layer_network().load_network("../datasets/imdb_gml.gml",directed=True,input_type="gml")
save this network as edgelist for node2vec
multilayer_network.save_network("../datasets/test.edgelist")
## call a specific embedding binary --- this is not limited to n2v
train_node2vec_embedding.call_node2vec_binary("../datasets/test.edgelist","../datasets/test_embedding.emb",binary="../bin/node2vec",weighted=False)
## preprocess and check embedding
multilayer_network.load_embedding("../datasets/test_embedding.emb")
## visualize embedding
embedding_visualization.visualize_embedding(multilayer_network)
## output embedded coordinates as JSON
output_json = embedding_tools.get_2d_coordinates_tsne(multilayer_network,output_format="json")
with open('../datasets/embedding_coordinates.json', 'w') as outfile:
json.dump(output_json, outfile)
directed=False).add_dummy_layers()
multilayer_network.basic_stats()
# use embedding to first initialize the nodes..
# call a specific n2v compiled binary
train_node2vec_embedding.call_node2vec_binary(
"../datasets/IntactEdgelistedges.txt",
"../datasets/test_embedding.emb",
binary="../bin/node2vec",
weighted=False)
# preprocess and check embedding -- for speed, install parallel tsne from https://github.com/DmitryUlyanov/Multicore-TSNE, py3plex knows how to use it.
multilayer_network.load_embedding("../datasets/test_embedding.emb")
output_positions = embedding_tools.get_2d_coordinates_tsne(
multilayer_network, output_format="pos_dict")
# custom layouts are part of the custom coordinate option
layout_parameters = {"iterations": 200}
layout_parameters['pos'] = output_positions # assign parameters
network_colors, graph = multilayer_network.get_layers(style="hairball")
partition = cw.louvain_communities(multilayer_network)
# select top n communities by size
top_n = 10
partition_counts = dict(Counter(partition.values()))
top_n_communities = list(partition_counts.keys())[0:top_n]
# assign node colors
color_mappings = dict(
def plot_intact_embedding(num_it):
# string layout for larger network -----------------------------------
multilayer_network = multinet.multi_layer_network().load_network(
"../datasets/intact02.gpickle", input_type="gpickle",
directed=False).add_dummy_layers()
multilayer_network.basic_stats()
# use embedding to first initialize the nodes..
# call a specific n2v compiled binary
train_node2vec_embedding.call_node2vec_binary(
"../datasets/IntactEdgelistedges.txt",
"../datasets/test_embedding.emb",
binary="../bin/node2vec",
weighted=False)
# preprocess and check embedding -- for speed, install parallel tsne from https://github.com/DmitryUlyanov/Multicore-TSNE, py3plex knows how to use it.
multilayer_network.load_embedding("../datasets/test_embedding.emb")
# load the positions and select the projection algorithm
output_positions = embedding_tools.get_2d_coordinates_tsne(
multilayer_network, output_format="pos_dict")
# custom layouts are part of the custom coordinate option
layout_parameters = {"iterations": num_it}
layout_parameters['pos'] = output_positions # assign parameters
network_colors, graph = multilayer_network.get_layers(style="hairball")
partition = cw.louvain_communities(multilayer_network)
# select top n communities by size
top_n = 10
partition_counts = dict(Counter(partition.values()))
top_n_communities = list(partition_counts.keys())[0:top_n]
# assign node colors
color_mappings = dict(
zip(top_n_communities,
[x for x in colors_default if x != "black"][0:top_n]))
network_colors = [
color_mappings[partition[x]]
if partition[x] in top_n_communities else "black"
for x in multilayer_network.get_nodes()
]
f = plt.figure()
# gravity=0.2,strongGravityMode=False,barnesHutTheta=1.2,edgeWeightInfluence=1,scalingRatio=2.0
hairball_plot(graph,
network_colors,
layout_algorithm="custom_coordinates_initial_force",
layout_parameters=layout_parameters,
nodesize=0.02,
alpha_channel=0.30,
edge_width=0.001,
scale_by_size=False)
f.savefig("../datasets/" + str(num_it) + "intact.png",
bbox_inches='tight',
dpi=300)
