examples/node2vec_main.py
def learn_embeddings(walks):
    '''
    Learn embeddings by optimizing the Skipgram objective using SGD.
    '''
    walks = [list(map(str, walk)) for walk in walks]
    model = Word2Vec(walks, size=args.dimensions, window=args.window_size, min_count=0, sg=1, workers=args.workers,
                     iter=args.iter)
    model.wv.save_word2vec_format(args.output)
    print("保存完毕")

    return

    Learn embeddings by optimizing the Skipgram objective using SGD.
    
Learn embeddings by optimizing the Skipgram objective using SGD.
Learn embeddings by optimizing the Skipgram objective using SGD .
def learn_embeddings(walks):
    walks = [list(map(str, walk)) for walk in walks]
    model = Word2Vec(walks, size=args.dimensions, window=args.window_size, min_count=0, sg=1, workers=args.workers,
                     iter=args.iter)
    model.wv.save_word2vec_format(args.output)
    print("保存完毕")
    return
def learn_embeddings ( walks ) : walks = [ list ( map ( str , walk ) ) for walk in walks ] model = Word2Vec ( walks , size = args . dimensions , window = args . window_size , min_count = 0 , sg = 1 , workers = args . workers , iter = args . iter ) model . wv . save_word2vec_format ( args . output ) print ( "保存完毕") return
Learn embeddings by optimizing the Skipgram objective using SGD.
Learn embeddings by optimizing the Skipgram objective using SGD .
'''
    Learn embeddings by optimizing the Skipgram objective using SGD.
    '''
walks