from karateclub.dataset import GraphReader
import networkx as nx
import matplotlib.pyplot as plt
reader = GraphReader("twitch")
engb = nx.read_gml(
'L:\Github Repos\playground\TwitchSocialNetworks\TwitchDataENGB.gml')
graph = nx.path_graph(engb)
nx.draw(graph)
plt.draw() # pyplot draw()
# y = reader.get_target()
# from karateclub import Diff2Vec
# model = Diff2Vec(diffusion_number=2, diffusion_cover=20, dimensions=16)
# model.fit(graph)
# X = model.get_embedding()
# from sklearn.model_selection import train_test_split
# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# from sklearn.metrics import roc_auc_score
# from sklearn.linear_model import LogisticRegression
# downstream_model = LogisticRegression(random_state=0).fit(X_train, y_train)
# y_hat = downstream_model.predict_proba(X_test)[:, 1]
# auc = roc_auc_score(y_test, y_hat)
# print('AUC: {:.4f}'.format(auc))
"""GraphReader example."""
from karateclub.dataset import GraphReader
reader = GraphReader("facebook")
graph = reader.get_graph()
target = reader.get_target()
import networkx as nx import community import numpy as np from karateclub.community_detection.overlapping import EgoNetSplitter, NNSED, DANMF, MNMF, BigClam from karateclub.node_embedding.neighbourhood import GraRep, DeepWalk, Walklets, NMFADMM, Diff2Vec, BoostNE from karateclub.community_detection.non_overlapping import EdMot, LabelPropagation from karateclub.node_embedding.structural import GraphWave from karateclub.node_embedding.attributed import BANE, TENE from karateclub.graph_embedding import Graph2Vec from karateclub.dataset import GraphReader dataset = "twitch" reader = GraphReader(dataset) graph = reader.get_graph() target = reader.get_target() features = reader.get_features() quit() #----------------------------------- # Graph2Vec example #----------------------------------- graphs = [nx.newman_watts_strogatz_graph(50, 5, 0.3) for _ in range(1000)] model = Graph2Vec() model.fit(graphs) model.get_embedding()
model.get_embedding()
#-------------------------
# GraphSet reader example
#-------------------------
reader = GraphSetReader("reddit10k")
graphs = reader.get_graphs()
y = reader.get_target()
#----------------------
# Graph reader example
#----------------------
reader = GraphReader("facebook")
graph = reader.get_graph()
target = reader.get_target()
#-------------------------------
# Graph2Vec attributed example
#-------------------------------
graphs = []
for i in range(50):
graph = nx.newman_watts_strogatz_graph(50, 5, 0.3)
nx.set_node_attributes(graph, {j: str(j) for j in range(50)}, "feature")
graphs.append(graph)
model = Graph2Vec(attributed=True)
if __name__ == "__main__":
import json
import matplotlib.pyplot as plt
# nodes: 189028 edges: 1046039
# network = json.load(open("./test/Epinions.json"))
# 37805 40701
# network = json.load(open("./test/Epinions_20_percent.json"))
# 18902 10418
# network = json.load(open("./test/Epinions_10_percent.json"))
# 2125 2831
# network = json.load(open("./test/epinions_5_percent.json"))
# 1889 109
# network = json.load(open("./test/epinions_1_percent.json"))
# network = json.load(open("./test/PolBooks.json"))
# g = network_centrality(network)
# network_centrality(network)
from karateclub.dataset import GraphReader
# print(GraphReader().get_features())
g = GraphReader().get_graph()
# print(GraphReader().get_target())
# g = nx.newman_watts_strogatz_graph(1000, 20, 0.05)
print(nx.info(g))
print(g.nodes())
# splitter = EgoNetSplitter(1.0)
# splitter.fit(g)
# print(splitter.get_memberships())
# plt.show()