old_contents: import networkx as nx
  from node2vec import Node2Vec
  
  # FILES
  EMBEDDING_FILENAME = './embeddings.emb'
  EMBEDDING_MODEL_FILENAME = './embeddings.model'
  
  # Create a graph
  graph = nx.fast_gnp_random_graph(n=100, p=0.5)
  
  # Precompute probabilities and generate walks
  node2vec = Node2Vec(graph, dimensions=64, walk_length=30, num_walks=200, workers=4)
  ## if d_graph is big enough to fit in the memory, pass tmp_dir which has enough disk space
  node2vec = Node2Vec(graph, dimensions=64, walk_length=30, num_walks=200, workers=4, tmp_dir="/mnt/tmp_data")
  
  # Embed
  model = node2vec.fit(window=10, min_count=1, batch_words=4)  # Any keywords acceptable by gensim.Word2Vec can be passed, `diemnsions` and `workers` are automatically passed (from the Node2Vec constructor)
  
  # Look for most similar nodes
  model.wv.most_similar('2')  # Output node names are always strings
  
  # Save embeddings for later use
  model.wv.save_word2vec_format(EMBEDDING_FILENAME)
  
  # Save model for later use
  model.save(EMBEDDING_MODEL_FILENAME)
  
new_contents: import networkx as nx
  from node2vec import Node2Vec
  
  # FILES
  EMBEDDING_FILENAME = './embeddings.emb'
  EMBEDDING_MODEL_FILENAME = './embeddings.model'
  
  # Create a graph
  graph = nx.fast_gnp_random_graph(n=100, p=0.5)
  
  # Precompute probabilities and generate walks
  node2vec = Node2Vec(graph, dimensions=64, walk_length=30, num_walks=200, workers=4)
  
  ## if d_graph is big enough to fit in the memory, pass temp_folder which has enough disk space
  # Note: It will trigger "sharedmem" in Parallel, which will be slow on smaller graphs
  #node2vec = Node2Vec(graph, dimensions=64, walk_length=30, num_walks=200, workers=4, temp_folder="/mnt/tmp_data")
  
  # Embed
  model = node2vec.fit(window=10, min_count=1, batch_words=4)  # Any keywords acceptable by gensim.Word2Vec can be passed, `diemnsions` and `workers` are automatically passed (from the Node2Vec constructor)
  
  # Look for most similar nodes
  model.wv.most_similar('2')  # Output node names are always strings
  
  # Save embeddings for later use
  model.wv.save_word2vec_format(EMBEDDING_FILENAME)
  
  # Save model for later use
  model.save(EMBEDDING_MODEL_FILENAME)
  
subject: add relevant example