aff = run_rwr(subgraph, 0.2, 1000)
y_test = []
for node1, node2 in node_pairs:
y_test.append(aff[int(node1)][int(node2)])
print(
f"ROC AUC Score with Random Walk: {roc_auc_score(df['link'], y_test)}\n"
f"Average Precision with Random Walk: {average_precision_score(df['link'], y_test)}"
)
if __name__ == '__main__':
nodes_file = "data/facebook_large/musae_facebook_target.csv"
edges_file = "data/facebook_large/musae_facebook_edges.csv"
features_file = 'data/facebook_large/musae_facebook_features.json'
facebook_graph = read_graph(nodes_file, edges_file, features_file,
'facebook')
subgraph, df = create_samples(facebook_graph)
node_pairs = [tuple(x) for x in df[['id_1', 'id_2']].to_numpy()]
link_prediction_with_metrics(subgraph, node_pairs, df)
link_prediction_with_random_walk(subgraph, node_pairs, df)
nodes_file = "data/git_web_ml/musae_git_target.csv"
edges_file = "data/git_web_ml/musae_git_edges.csv"
features_file = 'data/git_web_ml/musae_git_features.json'
git_graph = read_graph(nodes_file, edges_file, features_file, 'git')
subgraph, df = create_samples(git_graph)
node_pairs = [tuple(x) for x in df[['id_1', 'id_2']].to_numpy()]
sorted_hubs = sorted(hubs.keys(), key=lambda x: hubs[x], reverse=True)
with open(file_hubs, 'w') as f:
for user in sorted_hubs:
f.write(str(user) + " : " + str(hubs[user]) + "\n")
# Auth
sorted_auth = sorted(auth.keys(), key=lambda x: auth[x], reverse=True)
with open(file_auth, 'w') as f:
for user in sorted_auth:
f.write(str(user) + " : " + str(auth[user]) + "\n")
# Hubs Print
graph = read_graph(file)
pos = nx.spring_layout(graph)
nx.draw(graph, pos, with_labes=True, font_weight='bold', **options)
plt.show()
lista = sorted_pr
nx.draw(graph, pos, with_labes=True, font_weight='bold', **options)
nx.draw_networkx_nodes(graph, pos,
nodelist=lista[33:35],
node_color='b',
node_size=10,
alpha=0.8)
plt.show()
plt.show()
"""
Plotar Grafo Direcionado e Não Direcionado
"""
import networkx as nx
import matplotlib.pyplot as plt
from helper import read_graph, read_digraph
# print options
file = "graphs\clean_graph.txt"
options = {
'node_color': 'red',
'node_size': 5,
'width': 0.5
}
# read graph
graph = read_graph()
# plot
pos = nx.spring_layout(graph)
nx.draw(graph, pos, with_labes=True, font_weight='bold', **options)
plt.show()