text: 
  import numpy as np
  
  from ge.classify import read_node_label, Classifier
  from ge import LINE
  from sklearn.linear_model import LogisticRegression
  
  import matplotlib.pyplot as plt
  import networkx as nx
  from sklearn.manifold import TSNE
  
  
  def evaluate_embeddings(embeddings):
      X, Y = read_node_label('../data/wiki/wiki_labels.txt')
      tr_frac = 0.8
      print("Training classifier using {:.2f}% nodes...".format(
          tr_frac * 100))
      clf = Classifier(embeddings=embeddings, clf=LogisticRegression())
      clf.split_train_evaluate(X, Y, tr_frac)
  
  
  def plot_embeddings(embeddings,):
      X, Y = read_node_label('../data/wiki/wiki_labels.txt')
  
      emb_list = []
      for k in X:
          emb_list.append(embeddings[k])
      emb_list = np.array(emb_list)
  
      model = TSNE(n_components=2)
      node_pos = model.fit_transform(emb_list)
  
      color_idx = {}
      for i in range(len(X)):
          color_idx.setdefault(Y[i][0], [])
          color_idx[Y[i][0]].append(i)
  
      for c, idx in color_idx.items():
          plt.scatter(node_pos[idx, 0], node_pos[idx, 1], label=c)
      plt.legend()
      plt.show()
  
  
  if __name__ == "__main__":
      G = nx.read_edgelist('../data/wiki/Wiki_edgelist.txt',
                           create_using=nx.DiGraph(), nodetype=None, data=[('weight', int)])
  
      model = LINE(G, embedding_size=128, order='second')
      model.train(batch_size=1024, epochs=50, verbose=2)
      embeddings = model.get_embeddings()
  
      evaluate_embeddings(embeddings)
      plot_embeddings(embeddings)
  